Industrial wastewater deep purification system based on nano catalytic membrane separation

Abstract

The invention relates to the technical field of industrial wastewater deep purification, and discloses an industrial wastewater deep purification system based on nano catalytic membrane separation, which comprises a pretreatment module, a catalytic degradation module, a membrane separation module, an intelligent control module and a regeneration maintenance module which are sequentially connected according to a treatment flow, the pretreatment module adjusts the water quality of inlet water to a range suitable for subsequent treatment through pH adjustment and suspended matter interception, that is, the pH is 2-11, and the turbidity is less than or equal to 50NTU; the catalytic degradation module is used for decomposing organic pollutants and mineralizing refractory substances under the excitation of ultraviolet / visible light by utilizing the photocatalytic characteristic of a nano catalytic membrane. The TiO2 / WO3 heterojunction nano catalytic membrane is adopted, so that deep oxidation of refractory pollutants such as phenol and sulfonamide antibiotics is realized, finally, the COD removal rate of 98.7% is achieved, and compared with a traditional photocatalytic process, the energy consumption is reduced by 54%.

Description

Technical Field

[0001] The present invention relates to the technical field of deep purification of industrial wastewater, and in particular to a deep purification system for industrial wastewater based on nanocatalytic membrane separation. Background Art

[0002] Deep purification of industrial wastewater refers to the use of key technologies such as advanced oxidation, catalytic degradation, and precision membrane separation to remove difficult-to-degrade organic matter (such as benzene series, halogenated hydrocarbons), heavy metal ions, and trace toxic substances from industrial wastewater that still does not meet the standards after conventional treatment (such as physical precipitation and biochemical reaction). This reduces the chemical oxygen demand (COD) of the effluent to below 50 mg / L, the suspended solids (SS) to ≤10 mg / L, and the removal rate of characteristic pollutants to ≥95%, ultimately improving the water quality to meet industrial reuse or strict emission standards. Its core goal is to achieve wastewater resource utilization and near-zero emissions.

[0003] TiO2 catalysts are commonly used in the existing deep purification of industrial wastewater. The visible light utilization rate is less than 15%, and the hydroxyl radical yield is low, resulting in a large amount of residual difficult-to-degrade organic matter such as phenol and sulfonamide antibiotics in industrial wastewater. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides an industrial wastewater deep purification system based on nanocatalytic membrane separation, which solves the problem of large amounts of residual difficult-to-degrade organic matter such as phenol and sulfonamide antibiotics in industrial wastewater due to the TiO2 catalyst commonly used in existing industrial wastewater deep purification.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an industrial wastewater deep purification system based on nanocatalytic membrane separation, comprising the following pretreatment module, catalytic degradation module, membrane separation module, intelligent control module and regeneration maintenance module connected in the order of the treatment process; The pretreatment module adjusts the influent water quality to a range suitable for subsequent treatment by pH adjustment and suspended solids interception, i.e. pH 2-11 and turbidity ≤ 50 NTU; The catalytic degradation module utilizes the photocatalytic properties of the nanocatalytic membrane to decompose organic pollutants and mineralize refractory substances under the excitation of ultraviolet / visible light; The membrane separation module separates water molecules from pollutants through selective permeation, controlling the water flux and pressure parameters; The intelligent control module integrates data acquisition, process optimization algorithms and fault diagnosis functions, and dynamically adjusts the operating parameters of each module; The regeneration maintenance module is used for periodic cleaning procedures to remove membrane fouling.

[0006] Through the above technical solution: after the pretreatment module passes through the multi-stage grid, the pH of the inlet water is controlled at 2.5-10.5 with 0.1mol / L acid / alkali solution in the pH adjustment tank, and 15-30mg / L PAC and 0.5-1.2mg / L anion PAM are added for flocculation and sedimentation to ensure that the effluent turbidity is ≤15NTU; the catalytic degradation module uses TiO2 / WO3 heterojunction nanofilm to generate active free radicals under 365nm ultraviolet light, and the energy control submodule dynamically adjusts the irradiation intensity I (kW / m 2 ) and time t(min), when the light response index R>1.5, the enhanced irradiation mode is switched and maintained at 35±2℃ and pH=7.2±0.3; the membrane separation module operates with a 0.1μm ceramic membrane under a pressure difference of 0.15-0.35MPa, and the pressure regulation submodule is controlled in real time by the permeation flux degradation index φ, and the blockage point is predicted based on historical data; the intelligent control module integrates PLC and edge computing units, collects 12-dimensional sensor data, predicts the degradation path based on the LSTM network and outputs the optimal agent dosage D; the regeneration maintenance module is triggered according to the cumulative processing volume or flux attenuation rate>25%. , alternately use 40kHz ultrasonic waves and 0.1mol / L citric acid / 0.5%NaOH for online cleaning for 30 minutes, and restart after the verification and evaluation submodule confirms that the flux recovery rate ηJ ≥ 92% and the pressure difference recovery rate ηP ≥ 95%; actual operation shows: the COD removal rate of phenol and heavy metal industrial wastewater is greater than 98.5%, the membrane life is extended to 18 months, and the power consumption per ton of water is reduced to 1.8kWh, thereby achieving deep oxidation of difficult-to-degrade pollutants such as phenol and sulfonamide antibiotics, and ultimately achieving a COD removal rate of 98.7%, and reducing energy consumption by 54% compared with related photocatalytic processes.

[0007] Preferably, the catalytic degradation module comprises: Photocatalytic reaction submodule: Activates the active sites on the surface of the nanocatalytic membrane through light excitation, oxidizing the hydroxyl radicals of pollutants; Energy control submodule: Dynamically adjusts the UV light source power and irradiation duration according to real-time water quality parameters. Based on the real-time monitoring parameters TOC and UV-254, establishes the pollutant light response intensity index R, which is defined as: , Among them, α is the empirical coefficient, representing the contribution weight of the aromatic structure in the water sample to light absorption, ϵ is a small constant to prevent the denominator from being zero, and the index R is used to measure the response activity of the target pollutant to the photocatalytic reaction, thereby linking the intensity I of the ultraviolet light source with the irradiation time t, and as a feedback input, affecting the ultraviolet power density and reaction duration to achieve the maximum catalytic yield under unit energy consumption conditions. In order to optimize the balance between energy consumption and reaction effect, the catalytic efficiency function is further defined. : , Among them, ΔCOD represents the amount of COD removed per unit time. and are the current and optimal reaction temperature, pH and are the current and optimal pH values, γ and λ are the temperature and pH deviation penalty coefficients; Reaction environment control submodule: maintains the reaction system temperature (20-45°C) and pH value to ensure the catalytic reaction proceeds.

[0008] Preferably, the membrane separation module comprises: Pressure control submodule: By monitoring the transmembrane pressure difference and linking it with the variable frequency pump, the membrane surface shear force is maintained to reduce pollution deposition. The transmembrane pressure difference is dynamically adjusted through the online pressure sensor and the variable frequency pump speed linkage mechanism. Its change directly affects the permeate flux J and the compaction rate of the pollution layer. Therefore, it is necessary to build a control logic to optimize the relationship between this variable and the flux. For this purpose, the permeate flux degradation index Φ is introduced, which is defined as the ratio of the flux change rate to the transmembrane pressure difference change rate per unit time: , Wherein, dJ / dt represents the rate of change of permeation flux with time, dTMP / dt is the rate of change of transmembrane pressure difference, and δ is a stabilization factor used to prevent the denominator from approaching zero. Flux optimization submodule: predicts membrane fouling trends based on historical operating data and dynamically adjusts cross-flow rate and recovery rate; Anti-fouling early warning submodule: determines the membrane fouling stage by the slope of the pressure-flux curve and triggers the regeneration maintenance program.

[0009] Preferably, the intelligent control module includes: Multi-parameter fusion analysis submodule: integrates process parameters and constructs reaction kinetics models; Adaptive optimization submodule: predicts the pollutant degradation path through machine learning algorithms and dynamically adjusts UV intensity, reaction time and agent dosage; Fault diagnosis submodule: Identifies six typical faults based on abnormal parameter combinations and provides repair strategies.

[0010] Preferably, the regeneration maintenance module comprises: Physical cleaning submodule: loosens contaminants on the membrane surface through ultrasonic cavitation; Chemical cleaning submodule: injects customized cleaning agents to dissolve organic / inorganic pollutants; Verification and evaluation submodule: The cleaning effect is determined by the flux recovery rate and transmembrane pressure difference recovery rate, and whether to restart the treatment process is decided. In order to quantify the degree of membrane performance recovery during the cleaning process, the verification and evaluation submodule introduces the flux recovery index and pressure differential recovery index , respectively defined as follows: , in, and are the baseline flux and transmembrane pressure difference before pollution, and is the recovery value after cleaning.

[0011] Preferably, the adaptive optimization submodule uses an LSTM neural network to construct a pollutant degradation path prediction model, and dynamically optimizes the control parameters through the pollution potential function Φ(t). The pollution potential function is defined as: , in, is the pollution index response weight, λ is the system memory decay factor, and the LSTM network aims to minimize the residual pollution potential Φ(t+Δt) in the future time window Δt, and dynamically outputs the joint control instructions of ultraviolet intensity I, reaction time t and agent dosage D.

[0012] Preferably, the verification and evaluation submodule integrates the membrane fouling trend prediction function, and constructs the regeneration timing optimization function Γ(t) based on the flux decay rate and the transmembrane pressure difference growth rate: , Where ω1 and ω2 are the weight coefficients of flux attenuation and pressure difference increase, respectively, and J is the amount of water passing through the membrane per unit time. It represents the instantaneous rate of change of J, TMP is the pressure difference on both sides of the membrane, Indicates the instantaneous rate of change of TMP; When Γ(t) reaches the preset threshold, the physical-chemical collaborative cleaning program is triggered. After cleaning, if ηJ ≥ 85% and ηP ≥ 90% are satisfied, the system is restarted; otherwise, the deep cleaning mode is started and the contamination data is fed back to the LSTM network for model retraining.

[0013] Preferably, the modules are linked by a dynamic collaborative control matrix, specifically including: The energy regulation submodule of the catalytic degradation module shares the light response intensity index R to the intelligent control module in real time; The flux optimization submodule of the membrane separation module generates the cross-flow rate v based on the permeate flux degradation index Φ ∗ Instructions to synchronously adjust the flocculant injection rate of the pretreatment module; The verification results ηJ and ηP of the regeneration maintenance module are input into the fault diagnosis submodule of the intelligent control module to calibrate the weight w of the membrane fouling prediction model. i .

[0014] Preferably, the nanocatalytic membrane is a TiO2 / WO3 heterojunction composite structure, the surface active site energy band gap is 2.4-2.8eV, and hydroxyl radicals are generated under light of wavelength 300-550nm. The reaction environment control submodule maintains the catalytic efficiency function ηc to be maximized through the following coupling mechanism: When the real-time temperature θ deviates from the optimal value θ0=35°C, the Peltier semiconductor cooling / heating device is started; When the pH detection value deviates from pH0=7.2, the linkage pretreatment module injects acid-base regulator, and the response delay is ≤10s.

[0015] Preferably, the purification system is connected to a cloud interconnection subsystem, which is implemented through the 5G / industrial Ethernet protocol. The edge computing node uploads multi-parameter fusion data to the cloud platform in real time to generate a cross-site collaborative control strategy. The remote expert diagnosis interface receives the abnormal parameter combination Δs value of the fault diagnosis submodule and dynamically issues the membrane cleaning formula optimization coefficient. The switching logic of the bypass channel in the emergency response mechanism is triggered by the fault threshold function Θ(t): , in, represents the current value of the jth key operating parameter, As its reference value, is the fault sensitivity weight, when The system automatically executes module isolation and bypass switching procedures to ensure uninterrupted processing chain and data integrity.

[0016] The present invention provides an industrial wastewater deep purification system based on nanocatalytic membrane separation. It has the following beneficial effects: 1. The present invention uses TiO2 / WO3 heterojunction nanocatalytic film to produce a concentration of more than 4.2×10 -4 The system dynamically adjusts the power density of the ultraviolet light source and the irradiation time based on the light response intensity index R with an empirical coefficient α of 0.85 and an anti-zero constant ε of 0.01. At the same time, it uses the catalytic efficiency function ηc for real-time optimization, thereby achieving deep oxidation of difficult-to-degrade pollutants such as phenol and sulfonamide antibiotics, and ultimately achieving a COD removal rate of 98.7%. Compared with traditional photocatalytic processes, it reduces energy consumption by 54%.

[0017] 2. The present invention monitors the membrane fouling trend in real time by setting the permeation flux degradation index φ with a stability factor δ of 0.01. When the index value φ is greater than 0.15, the anti-pollution mode is automatically triggered. The LSTM neural network is combined to predict and output the optimal cross-flow rate v* and recovery rate r*, and an intelligent early warning is performed based on the slope k of the pressure-flux curve. The regeneration program is then started and a customized chemical cleaning solution is coordinated. The verification and evaluation submodule uses the flux recovery rate ηJ ≥ 92% and the pressure difference recovery rate ηP ≥ 95% as the system restart criteria, thereby achieving the goal of maintaining the membrane flux at a stable level of 85 liters / square meter·hour, extending the chemical cleaning cycle to 480 hours, extending the membrane life to 26 months, and reducing the flux attenuation rate by 58%.

[0018] 3. The present invention constructs a dynamic collaborative matrix based on the OPCUA protocol: the catalytic degradation module shares the light response intensity index R to the collaborative network in real time. When the R value exceeds 1.8, the linkage mechanism is triggered, and the polyaluminum chloride PAC dosage of the pretreatment module is increased to 27 mg / L, achieving a 42% reduction in the UV254 absorbance of the influent. The membrane separation module generates a flocculant compensation function based on the permeation flux degradation index φ, and dynamically regulates the anionic polyacrylamide PAM concentration in the range of 0.4 to 1.5 mg / L; the regeneration and maintenance module feeds back the flux recovery rate ηJ and the pressure difference recovery rate ηP to the intelligent control module, calibrates the model weight wi through the Bayesian algorithm, and increases the transmembrane pressure difference weight w4 from the baseline value of 0.22 to 0.28 when ηP is lower than 92%, thereby achieving the effect of making the system face a COD value fluctuation of ±30% with a water outlet compliance rate of more than 98%, reducing flocculant consumption, and reducing the cost of treating a ton of water. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a system framework diagram of an industrial wastewater deep purification system based on nanocatalytic membrane separation proposed by the present invention; Figure 2 This is a schematic diagram of a catalytic degradation module of an industrial wastewater deep purification system based on nanocatalytic membrane separation proposed by the present invention; Figure 3 This is a schematic diagram of the system flow of an industrial wastewater deep purification system based on nanocatalytic membrane separation proposed by the present invention; Figure 4 This is a data flow diagram of an industrial wastewater deep purification system based on nanocatalytic membrane separation proposed by the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] Please see the attached Figure 1 -Attached Figure 4 , an embodiment of the present invention provides an industrial wastewater deep purification system based on nanocatalytic membrane separation, comprising the following pretreatment module, catalytic degradation module, membrane separation module, intelligent control module and regeneration maintenance module connected in the order of the treatment process; The pretreatment module adjusts the influent water quality to a range suitable for subsequent treatment by pH adjustment and suspended solids interception, i.e. pH 2-11 and turbidity ≤ 50 NTU; The catalytic degradation module utilizes the photocatalytic properties of the nanocatalytic membrane to decompose organic pollutants and mineralize refractory substances under the excitation of ultraviolet / visible light; The membrane separation module separates water molecules from pollutants through selective permeation, controlling the water flux and pressure parameters; The intelligent control module integrates data acquisition, process optimization algorithms and fault diagnosis functions, and dynamically adjusts the operating parameters of each module; The regeneration maintenance module is used for periodic cleaning procedures to remove membrane fouling.

[0022] Specifically, the pretreatment module first intercepts large suspended solids through a multi-stage grid, and then accurately controls the inlet pH to the range of 2.5-10.5 with 0.1 mol / L acid / base solution in the pH adjustment tank to prevent extreme pH from damaging the catalytic membrane. At the same time, 15-30 mg / L of polychlorinated water and 0.5-1.2 mg / L of anionic PAM are added for flocculation and sedimentation to ensure that the turbidity is ≤15 NTU to meet the pollution resistance threshold of the nanocatalytic membrane; the catalytic degradation module uses a TiO2 / WO3 heterojunction nanomembrane to excite electron-hole pairs under the irradiation of a 365nm wavelength ultraviolet light source to generate OH free radicals. The energy control submodule monitors the TOC and UV254 values ​​in real time and dynamically adjusts the ultraviolet irradiation intensity I (kW / m 2) and duration t (min), when the light response intensity index R>1.5, the enhanced irradiation mode is started, and the reaction temperature is maintained at 35±2℃ and pH=7.2±0.3 simultaneously; the membrane separation module uses a 0.1μm ceramic membrane to operate under a transmembrane pressure difference of 0.15-0.35MPa, and the permeation flux degradation index φ is monitored online through the pressure control submodule. When φ>0.15, the cross-flow rate is automatically increased to 2.5m / s, and the critical blockage point is predicted in combination with historical pollution data; the intelligent control module integrates PLC and edge computing units, collects 12-dimensional sensor data, including ORP, UV transmittance, ion concentration, etc., based on LSTM network The network predicts pollutant degradation pathways and outputs the optimal reagent dosage D. The regeneration and maintenance module initiates a physical-chemical collaborative cleaning procedure based on cumulative processing volume or a flux decay rate greater than 25%, alternating between 40kHz ultrasonic waves and 0.1mol / L citric acid / 0.5% NaOH solution for online cleaning for 30 minutes. The verification and evaluation submodule ultimately restarts the system when the flux recovery rate ηJ ≥ 92% and the pressure differential recovery rate ηP ≥ 95%. Actual operational data demonstrates that the system's treatment of industrial wastewater containing phenol and heavy metals achieves the following results: COD removal rate greater than 98.5%, membrane life extended to 18 months, and power consumption reduced to 1.8kWh per ton of water.

[0023] See attached Figure 2 , the catalytic degradation module includes: Photocatalytic reaction submodule: Activates the active sites on the surface of the nanocatalytic membrane through light excitation, oxidizing the hydroxyl radicals of pollutants; Energy control submodule: Dynamically adjusts the UV light source power and irradiation duration according to real-time water quality parameters. Based on the real-time monitoring parameters TOC and UV-254, establishes the pollutant light response intensity index R, which is defined as: , Among them, α is the empirical coefficient, representing the contribution weight of the aromatic structure in the water sample to light absorption, ϵ is a small constant to prevent the denominator from being zero, and the index R is used to measure the response activity of the target pollutant to the photocatalytic reaction, thereby linking the intensity I of the ultraviolet light source with the irradiation time t, and as a feedback input, affecting the ultraviolet power density and reaction duration to achieve the maximum catalytic yield under unit energy consumption conditions. In order to optimize the balance between energy consumption and reaction effect, the catalytic efficiency function is further defined. : , Among them, ΔCOD represents the amount of COD removed per unit time. and are the current and optimal reaction temperature, pH and are the current and optimal pH values, γ and λ are the temperature and pH deviation penalty coefficients; Reaction environment control submodule: maintains the reaction system temperature (20-45°C) and pH value to ensure the catalytic reaction proceeds.

[0024] Specifically, the photocatalytic reaction submodule uses a TiO2 / WO3 heterojunction nanofilm with a specific surface area of ​​not less than 180 square meters per gram and a pore size distribution range of 5 to 20 nanometers. Under the excitation mode of an ultraviolet light source with a wavelength of 365 nanometers or a visible light with a wavelength of not less than 420 nanometers, it generates electron-hole pairs with a quantum yield of or greater than 0.32 through band matching excitation, inducing the generation of electron paramagnetic resonance detection concentration greater than 2.5×10 -4 The energy regulation submodule acquires TOC and UV254 data in real time through an online spectrometer with a sampling frequency of 5 Hz, and calculates the light response intensity index R with an empirical coefficient α of 0.85 and an anti-zero constant ε of 0.01. When the R value is greater than 1.5, it is determined that the pollutant has increased photosensitivity, and the UV power density is automatically increased from the normal mode of 50 mW / cm2 to 80 mW / cm2, and the irradiation time is extended from the baseline value of 90 seconds to 120 seconds. At the same time, based on the catalytic efficiency function ηc with a temperature offset penalty coefficient γ of 0.15 kWh / °C and a pH offset penalty coefficient λ of 0.08 kWh / pH unit, real-time optimization is performed to dynamically balance the COD removal rate ΔCOD with a target value of not less than 45 mg / L / min and the energy consumption cost. For every 1°C the temperature deviates from the optimal value by 35°C, a penalty energy consumption of 0.15 kWh is added. For every 0.1 unit of H deviating from the optimal value of 7.2, an energy penalty of 0.08 kWh is imposed. The reaction environment control submodule monitors the reactor's internal environmental conditions using distributed thermocouples with an accuracy of ±0.3 degrees Celsius and a pH / ORP composite electrode with a response time of less than 5 seconds. When a local hotspot exceeding 40 degrees Celsius is detected, a Peltier semiconductor cooler with a cooling rate of 4 degrees Celsius per minute is activated. When the pH fluctuates outside the range of 7.2±0.3, the pretreatment module is activated to inject 0.1 mol / L H2SO4 or NaOH solution. The PID control flow rate error is maintained within ±3%. In petrochemical wastewater containing 200 mg / L of phenol, the system, through an R-value-driven dynamic irradiation strategy, increased the COD removal rate to 98.7%, an improvement over the 91.2% achieved in the fixed-power mode. At the same time, energy consumption per ton of water was reduced to 2.3 kWh, a 54% decrease compared to traditional photocatalytic processes. The catalytic activity decay rate on the membrane surface was controlled to less than 5% per 100 hours.

[0025] See attached Figure 3 , the membrane separation module includes: Pressure control submodule: By monitoring the transmembrane pressure difference and linking it with the variable frequency pump, the membrane surface shear force is maintained to reduce pollution deposition. The transmembrane pressure difference is dynamically adjusted through the online pressure sensor and the variable frequency pump speed linkage mechanism. Its change directly affects the permeate flux J and the compaction rate of the pollution layer. Therefore, it is necessary to build a control logic to optimize the relationship between this variable and the flux. For this purpose, the permeate flux degradation index Φ is introduced, which is defined as the ratio of the flux change rate to the transmembrane pressure difference change rate per unit time: , Wherein, dJ / dt represents the rate of change of permeation flux with time, dTMP / dt is the rate of change of transmembrane pressure difference, and δ is a stabilization factor used to prevent the denominator from approaching zero. Flux optimization submodule: predicts membrane fouling trends based on historical operating data and dynamically adjusts cross-flow rate and recovery rate; Anti-fouling early warning submodule: determines the membrane fouling stage by the slope of the pressure-flux curve and triggers the regeneration maintenance program.

[0026] Specifically, the pressure control submodule adopts a 0.1-micron tubular ceramic membrane with an alumina matrix and a porosity of 42%. It monitors the transmembrane pressure difference TMP in real time through a high-precision pressure sensor with a range of 0 to 1.0 MPa and a measurement error of no more than plus or minus 0.5%. When the transmembrane pressure difference TMP is detected to be greater than 0.3 MPa, the submodule links a variable frequency pump with a power of 7.5 kilowatts and a response time of less than 2 seconds to increase the membrane surface shear force to 3.2 meters per second. At this time, the Reynolds number Re exceeds 8000. Under this state, the calculation formula of the permeation flux degradation index φ is set to be stable. The factor δ is 0.01 to avoid the zero drift problem during calculation. When the permeation flux degradation index φ is greater than 0.15, the system automatically starts the anti-pollution mode. This value indicates that the flux decay rate is more than 15% faster than the pressure difference rise rate. The flux optimization submodule builds a membrane fouling trend model based on an LSTM neural network with 128 nodes in the hidden layer and more than 100,000 training data sets. The model input parameters include twelve-dimensional features such as the historical gradient of the transmembrane pressure difference TMP, the flux decay rate, and the influent COD fluctuation. The optimal cross-flow rate v* in the next 30 minutes is output, and its control range is 1.8 to 3.5 meters per second, and the optimal recovery rate r*, set between 55% and 75%, the model output is dynamically adjusted by a PID controller with an actuator positioning accuracy of plus or minus 0.05 meters per second; the anti-pollution warning submodule fits the slope k of the pressure-flux curve in real time with a sampling period of once per second. When the slope k value is greater than 0.08 MPa hour per liter in 5 consecutive sampling periods, the system determines that it is currently in the contaminated layer compaction stage; or when the slope k value undergoes a mutation of more than 50%, it warns of the risk of irreversible contamination. Once any of the above conditions are met, When a condition is met, the system immediately triggers the regeneration maintenance program and automatically generates a diagnostic report that distinguishes between organic and inorganic pollution types. When treating oily wastewater with an emulsified oil concentration of 150 mg per liter, the pressure control submodule stabilizes the membrane flux at 85 liters per square meter per hour, and its flux decay rate does not exceed 12% per 24 hours, an increase of 58% compared with traditional membrane systems. At the same time, the membrane chemical cleaning cycle is extended to 480 hours, an improvement over the baseline value of 200 hours. After regeneration maintenance, the membrane flux recovery rate ηJ reaches 93.5%, and its fluctuation range is within plus or minus 2.1%.

[0027] See attached Figure 4 , the intelligent control module includes: Multi-parameter fusion analysis submodule: integrates process parameters and constructs reaction kinetics models; Adaptive optimization submodule: predicts the pollutant degradation path through machine learning algorithms and dynamically adjusts UV intensity, reaction time and agent dosage; Fault diagnosis submodule: Identifies six typical faults based on abnormal parameter combinations and provides repair strategies.

[0028] Specifically, the multi-parameter fusion analysis submodule collects twelve-dimensional process parameters in real time through a distributed sensor network with a sampling frequency of 10 Hz, including pH, redox potential (ORP), turbidity, transmembrane pressure difference, UV intensity, total organic carbon (TOC), UV254 absorbance at 254 nm, temperature, conductivity, flow rate, reagent concentration, and membrane flux. Based on the time series, a reaction kinetic state matrix with a dimension of 12 by 60 and a time window of 1 minute is constructed. The matrix is ​​reduced to three principal components with a cumulative contribution rate of more than 92% through principal component analysis. These principal components are input into a Dropout matrix with three hidden layers, 256 neurons, and The LSTM neural network with an out rate of 0.2 is trained to generate a pollutant degradation potential function Ψ(t) with the pollution index response weight coefficients k1 as 0.38, k2 as 0.42, k3 as 0.20 and the system memory decay factor λ as 0.05 per second. The minute-level prediction of the degradation path of organic pollutants is achieved, and the prediction error does not exceed 8%. The adaptive optimization submodule aims to minimize the residual pollution potential ΔΨ in the next 10-minute time window. The NSGA-II optimization algorithm is used to dynamically output the UV intensity adjustment amount ΔI with a range of ±15% of the rated power; the output reaction time increment Δt with a range of The range is ±30 seconds; and the dosage of the reagent D is controlled between 0.1 and 0.5 liters per minute and is executed by a metering pump with an accuracy of ±1.5%. These optimization instructions are sent to the actuator through the OPC protocol with a response delay of no more than 200 milliseconds. The fault diagnosis submodule uses the Mahalanobis distance algorithm to construct a twelve-dimensional parameter offset model, and its weight coefficient wi is determined by Fisher discriminant analysis. When it is detected that the parameter offset distance Δs is greater than 2.5, the system triggers the fault diagnosis tree function to identify and distinguish six typical faults, including: the transmembrane pressure difference TMP mutation amplitude exceeds 0.1 MPa per minute and the flux decreases by more than 0.1 MPa per minute; Membrane blockage greater than 40%; light source failure with UV intensity 30% lower than the set value; sensor drift with a standard deviation less than 0.01 within 5 consecutive sampling cycles. For identified faults, the system automatically generates matching instructions from the repair strategy library. For example, when the diagnosis is membrane blockage, the regeneration module is started and the recovery rate is reduced to 50% at the same time. In the treatment of heavy metal-containing wastewater, this module reduced the fluctuation range of system parameters by 76%, the tracking error of the process set point was less than 3%, and the fault identification accuracy rate was as high as 95.3% after 2000 hours of continuous operation. At the same time, the cost of treating a ton of water was reduced to 0.8 yuan, a decrease of 67% compared with manual control.

[0029] The regeneration maintenance module includes: Physical cleaning submodule: loosens contaminants on the membrane surface through ultrasonic cavitation; Chemical cleaning submodule: injects customized cleaning agents to dissolve organic / inorganic pollutants; Verification and evaluation submodule: The cleaning effect is determined by the flux recovery rate and transmembrane pressure difference recovery rate, and whether to restart the treatment process is decided. In order to quantify the degree of membrane performance recovery during the cleaning process, the verification and evaluation submodule introduces the flux recovery index and pressure differential recovery index , respectively defined as follows: , in, and are the baseline flux and transmembrane pressure difference before pollution, and is the recovery value after cleaning.

[0030] Specifically, the physical cleaning submodule uses an ultrasonic array with a frequency of 40 kHz, a sound intensity of 0.8 watts per square centimeter, and a fluctuation range controlled within ±5%, ensuring that the cavitation effect threshold is greater than 0.35 MPa. The action time is 15 minutes. Scanning electron microscopy observation shows that the process generates microjets with a speed of more than 100 meters per second through cavitation collapse, which can strip off at least 80% of the loose contamination layer on the membrane surface. The chemical cleaning submodule automatically matches and injects the pollution type diagnosis results of the intelligent control module. Specific cleaning formula: For organic pollution, a mixture of 0.5% sodium hydroxide and 0.1% ethylenediaminetetraacetic acid is injected at a temperature of 45±2 degrees Celsius and a flow rate of 1.2 meters per second; for inorganic scaling, a 0.2 mol / L citric acid solution with a pH value of 2.5 and a temperature controlled at 50±1 degrees Celsius is used for 20 minutes of circulation cleaning. The chemical agent forms Taylor vortices with a Taylor number greater than 1000 in the membrane pores, enhancing the mass transfer process and achieving a dissolution efficiency of more than 93%. The results of inductively coupled plasma mass spectrometry are shown in Figure 2. The residual amount detected by the instrument is less than 50 mg per square meter. The verification and evaluation submodule measures the flux recovery index ηJ and the pressure difference recovery index ηP immediately after cleaning. The benchmark flux value J0 is set to 85 liters per square meter hour and the benchmark transmembrane pressure difference TMP0 is 0.25 MPa. When the flux recovery index ηJ reaches or exceeds 92% and the pressure difference recovery index ηP reaches or exceeds 95%, it is determined that the cleaning is effective and the system is allowed to restart. If it does not meet the standards, the deep cleaning mode with a total duration of no more than 60 minutes is started. This mode alternates Two rounds of coordinated physical and chemical cleaning were performed. When treating diatomaceous earth-containing papermaking wastewater with a suspended solids concentration of 500 mg / L, the regeneration and maintenance module reduced the membrane fouling index FI value from greater than 3.5 to less than 1.2, and the flux recovery rate ηJ reached 96.2%, with a fluctuation range within ±1.8%, an improvement of 32% over traditional cleaning methods. At the same time, the membrane life was extended to 22 months. X-ray diffraction detection showed that the crystal structure of the membrane surface changed by less than 3%, and the consumption of chemical agents was reduced by 41%.

[0031] See attached Figure 4The adaptive optimization submodule uses the LSTM neural network to build a pollutant degradation path prediction model and dynamically optimizes the control parameters through the pollution potential function Φ(t). The pollution potential function is defined as: , in, is the pollution index response weight, λ is the system memory decay factor, and the LSTM network aims to minimize the residual pollution potential Φ(t+Δt) in the future time window Δt, and dynamically outputs the joint control instructions of ultraviolet intensity I, reaction time t and agent dosage D.

[0032] Specifically, the LSTM neural network of the adaptive optimization submodule adopts a three-layer gated loop architecture, in which the hidden layer contains 256 neurons and the time step is set to 60. The network builds a dynamic optimization engine through the pollution potential function Φ(t): the twelve-dimensional parameters including UV254, TOC, and ORP collected in real time are normalized by mean-variance normalization, and a time series matrix is ​​constructed with a sampling period of once per minute as input to the pollution potential function Φ(t). The weight coefficients k1 of the pollution potential function Φ(t) are 0.38, k2 is 0.42, and k3 is 0.20. These values Determined by Spearman correlation analysis; its memory decay factor λ is set to 0.05 per second, corresponding to the influence weight of 20 seconds of historical data. The LSTM network calculates the residual pollution potential Φ(t+Δt) within the future time window Δt of 10 minutes through bidirectional propagation, and predicts the pollutant degradation path with an accuracy of less than 8% mean square error. The optimization engine adopts the NSGA-II multi-objective algorithm, with a constraint range of 30 to 80 milliwatts per square centimeter UV intensity I, a reaction time t with a range of ±30 seconds relative to the baseline value, and a dosage of the agent set between 0.1 and 0.5 liters per minute. The Pareto frontier is searched in the three-dimensional space of D. The optimization takes minimizing the residual pollution potential Φ(t+Δt) as the main goal and assigns a weight of 0.7; the secondary goal is to minimize the energy consumption function E, which is 0.02 times the ultraviolet intensity I plus 0.0015 times the agent dosage D, and assigns a weight of 0.3. Finally, the optimization engine outputs the optimal control instruction combination. For example, when a sudden increase in phenol concentration is predicted, an enhanced degradation strategy with an ultraviolet intensity I of 75 milliwatts per square centimeter, a reaction time t of 110 seconds, and an agent dosage D of 0.42 liters per minute will be generated. These optimization instructions are sent to On the execution side, the transmission delay does not exceed 200 milliseconds, and the UV intensity is adjusted by a constant current power supply with an accuracy error maintained within ±1.5%; the dosage of the reagent is precisely executed by a servo metering pump with a pulse frequency of up to 200 Hz and an accuracy error controlled within ±0.8%; when treating wastewater with a nitrobenzene concentration of 200 mg / L, the module increases the COD degradation rate to 52 mg / L / min, an increase of 136% compared to traditional PID control; at the same time, the energy consumption per unit pollutant treatment is reduced to 0.15 kWh per gram, saving 57% of energy; the tracking error of the control instruction is less than 2.5%.

[0033] The verification and evaluation submodule integrates the membrane fouling trend prediction function and constructs the regeneration timing optimization function Γ(t) based on the flux decay rate and the transmembrane pressure difference growth rate: , Where ω1 and ω2 are the weight coefficients of flux attenuation and pressure difference increase, respectively, and J is the amount of water passing through the membrane per unit time. It represents the instantaneous rate of change of J, TMP is the pressure difference on both sides of the membrane, Indicates the instantaneous rate of change of TMP; When Γ(t) reaches the preset threshold, the physical-chemical collaborative cleaning program is triggered. After cleaning, if ηJ ≥ 85% and ηP ≥ 90% are satisfied, the system is restarted; otherwise, the deep cleaning mode is started and the contamination data is fed back to the LSTM network for model retraining.

[0034] Specifically, the regeneration timing optimization function Γ(t) is obtained by real-time integration of the flux decay rate dJ / dt in the range of 0 to 100 liters per square meter per hour and the transmembrane pressure difference growth rate dTMP / dt in the range of 0 to 0.5 MPa per minute. The function sets the weight coefficient ω1 dominated by flux decay to 0.65 and the weight coefficient ω2 auxiliary by pressure difference change to 0.35. When the function value Γ(t) is greater than the threshold value of 0.15 determined by the ROC curve analysis of 200 groups of pollution experiments and the area under the curve AUC reaches 0.94, the system automatically triggers the physical-chemical collaborative cleaning program. In the physical cleaning stage, the system starts with an ultrasonic array with a frequency of 40 kHz, a sound intensity controlled within the range of 0.8 watts per square centimeter ± 3%, and a cavitation bubble collapse pressure greater than 0.4 MPa for 15 minutes. In the chemical cleaning stage, a customized cleaning agent is injected according to the intelligent diagnosis results: for organic pollution, 0.5% sodium hydroxide plus 0.05% dodecyl at a temperature of 55 degrees Celsius is used. Sodium sulfate mixture; for inorganic scaling, a 0.1 mol / L citric acid solution with 0.01% corrosion inhibitor added and a temperature of 45 degrees Celsius is used. The cleaning process is supplemented by a pulsating flow with a frequency of 1 Hz and an amplitude of 10 kPa to enhance the mass transfer efficiency in the membrane pores. After the cleaning is completed, the system immediately measures the flux recovery index ηJ with a benchmark flux value of 85 liters per square meter hour as J0, and the pressure recovery index ηP with a benchmark transmembrane pressure difference of 0.25 MPa as TMP0. If ηJ reaches or exceeds When the ηP reaches or exceeds 92% and ηP reaches or exceeds 95%, that is, it is 7% higher than the statutory standard, the cleaning is considered effective, the system restarts and records the cleaning data. If it does not meet the standard, it enters the deep cleaning mode, alternating two rounds of ultrasonic and chemical enhancement treatments with a total duration of no more than 45 minutes. At the same time, 12 pollution feature data including flux decay rate dJ / dt curve, transmembrane pressure difference growth rate dTMP / dt curve, cleaning agent formula, recovery rate, etc. will be packaged into a 100-dimensional feature vector and fed back to the learning rate of 10 -4 , and retrained the LSTM network with a batch size of 32. In the treatment of coking wastewater with a chemical oxygen demand (COD) of 1500 mg / L, this module reduced the membrane fouling index (MFI) by 62%, extended the average cleaning cycle to 520 hours, and stabilized the flux recovery rate ηJ at 95.2%, with a fluctuation range within ±1.5%, an improvement of 41% over the traditional method. The membrane life reached 26 months, and scanning electron microscopy observations showed that the annual wear rate of the membrane surface was less than 0.8 microns.

[0035] Parameter linkage is achieved between modules through a dynamic collaborative control matrix, including: The energy regulation submodule of the catalytic degradation module shares the light response intensity index R to the intelligent control module in real time; The flux optimization submodule of the membrane separation module generates the cross-flow rate v based on the permeate flux degradation index Φ ∗ Instructions to synchronously adjust the flocculant injection rate of the pretreatment module; The verification results ηJ and ηP of the regeneration maintenance module are input into the fault diagnosis submodule of the intelligent control module to calibrate the weight w of the membrane fouling prediction model. i .

[0036] Specifically, the dynamic collaborative control matrix builds a real-time data flow closed loop between modules through the OPCUA protocol with a transmission delay of no more than 150 milliseconds. The energy regulation submodule of the catalytic degradation module synchronizes the light response intensity index R with a calculation period of 0.5 seconds to the LSTM prediction engine of the intelligent control module at a shared frequency of 10 times per second. When the R value is greater than 1.8, which corresponds to the polycyclic aromatic hydrocarbon pollution feature, the system triggers the flocculation enhancement mode. At this time, the intelligent control module outputs the polyaluminum chloride PAC addition rate correction coefficient α equal to 1.35 to the pretreatment module, and its addition amount is increased from 0.5 seconds to 1.5 seconds. The baseline value of 20 mg / L was increased to 27 mg / L, and the absorbance of the influent at an ultraviolet wavelength of 254 nm was ultimately reduced by 42% as measured by ultraviolet spectrophotometry, with its fluctuation range controlled within ±3%. The flux optimization submodule of the membrane separation module generates an optimal cross-flow rate v instruction based on the permeate flux degradation index φ. When the calculated value of φ is greater than 0.12, the system determines that the membrane fouling is accelerating and outputs an optimal cross-flow rate v instruction set in the range of 1.5 to 3.0 meters per second. This instruction adjusts the speed of the variable frequency pump in real time through the ModbusTCP protocol to ensure a response time shorter than 800 milliseconds. At the same time, the submodule sends the flocculant dosage compensation function β equal to 0.45 multiplied by the natural logarithm of (φ plus 0.2) to the pretreatment module to control the concentration of anionic polyacrylamide PAM within the range of 0.4 to 1.5 mg / L. The verification results ηJ and ηP of the regeneration maintenance module have a measurement accuracy of ±1.2%. After input into the fault diagnosis submodule, the Bayesian update algorithm is used to calibrate the membrane fouling model weight wi. When the transmembrane pressure difference recovery rate ηP is lower than 92%, the transmembrane pressure difference weight w4 is increased to 0.28, compared with its baseline value of 0.22. When the flux recovery rate ηJ is lower than 90%, the flux attenuation weight w7 is enhanced to 0.35. Compared with its baseline value of 0.25, after weight calibration, the model prediction error is reduced to less than 5.7%; when treating printing and dyeing wastewater with a chemical oxygen demand (COD) of 800 mg per liter, this synergistic mechanism improves the system's ability to withstand load shock by 3.2 times. Specifically, even if the influent COD fluctuates by ±30%, the system's effluent compliance rate can still exceed 98%. At the same time, the flocculant consumption is reduced by 31%, reducing the cost per ton of water by 0.24 yuan; the accuracy of membrane pollution prediction is also improved to 96.8%.

[0037] The nanocatalytic film is a TiO2 / WO3 heterojunction composite structure with a surface active site energy band gap of 2.4-2.8eV. It produces hydroxyl radicals under light of 300-550nm wavelength. The reaction environment control submodule maintains the catalytic efficiency function ηc to be maximized through the following coupling mechanism: When the real-time temperature θ deviates from the optimal value θ0=35°C, the Peltier semiconductor cooling / heating device is started; When the pH detection value deviates from pH0=7.2, the linkage pretreatment module injects acid-base regulator, and the response delay is ≤10s.

[0038] Specifically, the TiO2 / WO3 heterojunction nanocatalytic membrane was prepared by a sol-gel-hydrothermal coupling method with a TiO2:WO3 molar ratio of 1:0.8 and treated at a calcination temperature of 550±10 degrees Celsius. X-ray diffraction characterization showed that its structure was the coexistence of anatase TiO2 phase that complies with the JCPDS21-1272 standard and monoclinic WO3 phase that complies with the JCPDS43-1035 standard. Ultraviolet diffuse reflectance spectroscopy determined that the band gap of the membrane was 2.65 electron volts, with a measurement error of no more than ±0.05 electron volts. Under irradiation with ultraviolet light of a wavelength of 365 nanometers and a photon energy of 3.4 electron volts, or under irradiation with visible light of a wavelength of 450 nanometers and a photon energy of 2.75 electron volts, the membrane achieved efficient electron-hole separation through a type II heterojunction mechanism, with a transient fluorescence lifetime of more than 12 nanoseconds and a surface active site density of up to 5.3×10 per square centimeter. 15 sites, its BET specific surface area reaches 192 square meters per gram, and the hydroxyl radical yield is greater than 4.2×10 -4 Moles per liter per minute, this value is measured by the salicylic acid fluorescence probe method, the reaction environment control submodule monitors the environment in the reactor in real time through a thermocouple array with a resolution of 0.1 degrees Celsius and a pH / ORP composite electrode with a response time of less than 3 seconds. When the temperature θ is detected to deviate from the optimal value θ0 by 35 degrees Celsius, which corresponds to the peak range of the catalytic efficiency function ηc, the system starts a Peltier semiconductor refrigeration device with a cooling power of not less than 120 watts and a temperature change rate of 4 degrees Celsius per minute to control the temperature fluctuation within the range of ±0.8 degrees Celsius. In the catalytic efficiency function ηc, the temperature penalty coefficient γ is set to 0.18 kilowatt-hours per degree Celsius. When the pH detection value deviates from the optimal value pH0 set at the Zeta potential zero point to 7.2 The system links the metering pump with a flow accuracy of ±1.2% in the pretreatment module, and injects 0.5 mol / L H2SO4 or NaOH solution for adjustment. The process uses a PID controller with an integral time constant of 8 seconds to achieve a response delay of pH callback to 7.2±0.15 within 7.5 seconds. In the function ηc, the pH offset penalty coefficient λ is set to 0.12 kWh per pH unit. When treating pharmaceutical wastewater containing sulfonamide antibiotics with a concentration of 200 mg / L, the collaborative control system increases the catalytic efficiency function ηc to 0.85, with a fluctuation range of within ±0.03, which is 51% higher than that of the uncontrolled system. The membrane catalytic activity decay rate is less than 3% per 500 hours. X-ray photoelectron spectroscopy detects Ti on the membrane surface. 3+ The change in defect concentration does not exceed 8%, and the energy consumption per unit pollutant degradation is reduced to 0.11 kWh per gram, saving 64% of energy.

[0039] See attached Figure 4 The purification system is connected to a cloud interconnection subsystem through the 5G / Industrial Ethernet protocol. The edge computing node uploads multi-parameter fusion data to the cloud platform in real time to generate a cross-site collaborative control strategy. The remote expert diagnosis interface receives the abnormal parameter combination Δs value of the fault diagnosis submodule and dynamically issues the membrane cleaning formula optimization coefficient. The switching logic of the bypass channel in the emergency response mechanism is triggered by the fault threshold function Θ(t): , in, represents the current value of the jth key operating parameter, As its reference value, is the fault sensitivity weight, when The system automatically executes module isolation and bypass switching procedures to ensure uninterrupted processing chain and data integrity.

[0040] Specifically, the cloud interconnection subsystem integrates a 5G module that supports NSA / SA dual-mode and has an uplink rate of no less than 100 megabits per second, and an industrial Ethernet that complies with the IEEE802.3 protocol and has a ring network redundancy time of less than 50 milliseconds, to jointly build a dual-channel data transmission architecture. It is equipped with an ARMCortex-A72 processor and an edge computing node with a computing power of 5 trillion operations per second. The LZW-Huffman hybrid algorithm is used to compress the twelve-dimensional process parameters in real time, and the compression rate is maintained at around 62%. The fluctuation range is controlled within ±3%, and it is uploaded to the cloud platform at a frequency of 5 times per second. The platform message queue capacity exceeds one million per second. The cloud platform uses a random forest algorithm containing 100 decision trees to analyze cross- Site data flow generates collaborative control strategies, which are encrypted with AES-256 and then sent to the edge for execution, ensuring that the end-to-end delay does not exceed 350 milliseconds. The remote expert diagnosis interface receives the abnormal parameter combination Δs value with a twelve-dimensional dimension and a quantization accuracy of 0.01 provided by the fault diagnosis sub-module. This interface is based on a case reasoning library that stores more than 5,000 sets of historical faults and dynamically outputs the membrane cleaning formula optimization coefficient. In the emergency response mechanism, the bypass channel switching is triggered in real time by the fault threshold function Θ(t). This function sets the transmembrane pressure difference weight coefficient γ1 to 0.30, the ultraviolet light intensity weight coefficient γ2 to 0.25, the current intensity weight coefficient γ3 to 0.20, and the permeation flux degradation index weight coefficient γ4 to 0.25. When Θ(t) is greater than or equal to 2.0, a threshold determined by Monte Carlo simulation with a 99% confidence interval, the system automatically isolates the faulty module, whose solenoid valve actuation time is shorter than 300 milliseconds, and switches to the backup membrane module channel with 30% redundancy, ensuring that the system flux fluctuation is less than plus or minus 5%.

[0041] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A deep purification system for industrial wastewater based on nanocatalytic membrane separation, characterized in that: It includes the following pretreatment module, catalytic degradation module, membrane separation module, intelligent control module and regeneration maintenance module connected in the order of treatment process; The pretreatment module adjusts the influent water quality to a range suitable for subsequent treatment by pH adjustment and suspended solids interception, i.e. pH 2-11 and turbidity ≤ 50 NTU; The catalytic degradation module utilizes the photocatalytic properties of the nanocatalytic film to decompose organic pollutants and mineralize refractory substances under ultraviolet / visible light excitation; The membrane separation module separates water molecules from pollutants through selective permeation, controlling the water flux and pressure parameters; The intelligent control module integrates data acquisition, process optimization algorithms and fault diagnosis functions to dynamically adjust the operating parameters of each module; The regeneration maintenance module is used for periodic cleaning procedures to remove membrane fouling.

2. The industrial wastewater deep purification system based on nanocatalytic membrane separation according to claim 1 is characterized in that: The catalytic degradation module comprises: Photocatalytic reaction submodule: Activates the active sites on the surface of the nanocatalytic membrane through light excitation, oxidizing the hydroxyl radicals of pollutants; Energy control submodule: Dynamically adjusts the UV light source power and irradiation duration according to real-time water quality parameters. Based on the real-time monitoring parameters TOC and UV-254, establishes the pollutant light response intensity index R, which is defined as: ， Among them, α is the empirical coefficient, representing the contribution weight of the aromatic structure in the water sample to light absorption, ϵ is a small constant to prevent the denominator from being zero, and the index R is used to measure the response activity of the target pollutant to the photocatalytic reaction, thereby linking the intensity I of the ultraviolet light source with the irradiation time t, and as a feedback input, affecting the ultraviolet power density and reaction duration to achieve the maximum catalytic yield under unit energy consumption conditions. In order to optimize the balance between energy consumption and reaction effect, the catalytic efficiency function is further defined. : ， Among them, ΔCOD represents the amount of COD removed per unit time. and are the current and optimal reaction temperature, pH and are the current and optimal pH values, γ and λ are the temperature and pH deviation penalty coefficients; Reaction environment control submodule: maintains the reaction system temperature (20-45°C) and pH value to ensure the catalytic reaction proceeds.

3. The industrial wastewater deep purification system based on nanocatalytic membrane separation according to claim 1 is characterized in that: The membrane separation module comprises: Pressure control submodule: By monitoring the transmembrane pressure difference and linking it with the variable frequency pump, the membrane surface shear force is maintained to reduce pollution deposition. The transmembrane pressure difference is dynamically adjusted through the online pressure sensor and the variable frequency pump speed linkage mechanism. Its change directly affects the permeate flux J and the compaction rate of the pollution layer. Therefore, it is necessary to build a control logic to optimize the relationship between this variable and the flux. For this purpose, the permeate flux degradation index Φ is introduced, which is defined as the ratio of the flux change rate to the transmembrane pressure difference change rate per unit time: ， Wherein, dJ / dt represents the rate of change of permeation flux with time, dTMP / dt is the rate of change of transmembrane pressure difference, and δ is a stabilization factor used to prevent the denominator from approaching zero. Flux optimization submodule: predicts membrane fouling trends based on historical operating data and dynamically adjusts cross-flow rate and recovery rate; Anti-fouling early warning submodule: determines the membrane fouling stage by the slope of the pressure-flux curve and triggers the regeneration maintenance program.

4. The industrial wastewater deep purification system based on nanocatalytic membrane separation according to claim 1 is characterized in that: The intelligent control module includes: Multi-parameter fusion analysis submodule: integrates process parameters and constructs reaction kinetics models; Adaptive optimization submodule: predicts the pollutant degradation path through machine learning algorithms and dynamically adjusts UV intensity, reaction time and agent dosage; Fault diagnosis submodule: Identifies six typical faults based on abnormal parameter combinations and provides repair strategies.

5. The industrial wastewater deep purification system based on nanocatalytic membrane separation according to claim 1 is characterized in that: The regeneration maintenance module includes: Physical cleaning submodule: loosens contaminants on the membrane surface through ultrasonic cavitation; Chemical cleaning submodule: injects customized cleaning agents to dissolve organic / inorganic pollutants; Verification and evaluation submodule: The cleaning effect is determined by the flux recovery rate and transmembrane pressure difference recovery rate, and whether to restart the treatment process is decided. In order to quantify the degree of membrane performance recovery during the cleaning process, the verification and evaluation submodule introduces the flux recovery index and pressure differential recovery index , respectively defined as follows: ， in, and are the baseline flux and transmembrane pressure difference before pollution, and is the recovery value after cleaning.

6. The industrial wastewater deep purification system based on nanocatalytic membrane separation according to claim 1 is characterized in that: The adaptive optimization submodule uses an LSTM neural network to construct a pollutant degradation path prediction model and dynamically optimizes the control parameters through the pollution potential function Φ(t), which is defined as: ， in, is the pollution index response weight, λ is the system memory decay factor, and the LSTM network aims to minimize the residual pollution potential Φ(t+Δt) in the future time window Δt, and dynamically outputs the joint control instructions of ultraviolet intensity I, reaction time t and agent dosage D.

7. The industrial wastewater deep purification system based on nanocatalytic membrane separation according to claim 1 is characterized in that: The verification and evaluation submodule integrates the membrane fouling trend prediction function and constructs the regeneration timing optimization function Γ(t) based on the flux decay rate and the transmembrane pressure difference growth rate: ， Where ω1 and ω2 are the weight coefficients of flux attenuation and pressure difference increase, respectively, and J is the amount of water passing through the membrane per unit time. It represents the instantaneous rate of change of J, TMP is the pressure difference on both sides of the membrane, Indicates the instantaneous rate of change of TMP; When Γ(t) reaches the preset threshold, the physical-chemical collaborative cleaning program is triggered. After cleaning, if ηJ ≥ 85% and ηP ≥ 90% are satisfied, the system is restarted; otherwise, the deep cleaning mode is started and the contamination data is fed back to the LSTM network for model retraining.

8. The industrial wastewater deep purification system based on nanocatalytic membrane separation according to claim 1 is characterized in that: Parameter linkage is achieved between modules through a dynamic collaborative control matrix, including: The energy regulation submodule of the catalytic degradation module shares the light response intensity index R to the intelligent control module in real time; The flux optimization submodule of the membrane separation module generates the cross-flow rate v based on the permeate flux degradation index Φ ∗ Instructions to synchronously adjust the flocculant injection rate of the pretreatment module; The verification results ηJ and ηP of the regeneration maintenance module are input into the fault diagnosis submodule of the intelligent control module to calibrate the weight w of the membrane fouling prediction model. i .

9. The industrial wastewater deep purification system based on nanocatalytic membrane separation according to claim 1 is characterized in that: The nanocatalytic membrane is a TiO2 / WO3 heterojunction composite structure with a surface active site energy band gap of 2.4-2.8eV. It produces hydroxyl radicals under light of 300-550nm wavelength. The reaction environment control submodule maintains the catalytic efficiency function ηc to be maximized through the following coupling mechanism: When the real-time temperature θ deviates from the optimal value θ0=35°C, the Peltier semiconductor cooling / heating device is started; When the pH detection value deviates from pH0=7.2, the linkage pretreatment module injects acid-base regulator, and the response delay is ≤10s.

10. The industrial wastewater deep purification system based on nanocatalytic membrane separation according to claim 1, characterized in that: The purification system is connected to a cloud-based interconnection subsystem, implemented via the 5G / Industrial Ethernet protocol. Edge computing nodes upload multi-parameter fusion data to the cloud platform in real time to generate a cross-site collaborative control strategy. The remote expert diagnosis interface receives the abnormal parameter combination Δs value from the fault diagnosis submodule and dynamically issues the membrane cleaning formula optimization coefficient. The bypass channel switching logic in the emergency response mechanism is triggered by the fault threshold function Θ(t): ， in, represents the current value of the jth key operating parameter, As its reference value, is the fault sensitivity weight, when The system automatically executes module isolation and bypass switching procedures to ensure uninterrupted processing chain and data integrity.

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