High-salinity wastewater treatment process system and method

Abstract

The invention is applicable to the technical field of high-salinity wastewater treatment, and provides a high-salinity wastewater treatment process system and method.The method is characterized in that a wastewater state evaluation module constructs a model according to osmotic pressure, pH and viscosity of wastewater and outputs an evaluation coefficient; the pollutant state evaluation module outputs an evaluation coefficient based on the concentrations of the organic matters, the inorganic matters and the particulate matters; the membrane material state evaluation module constructs a model according to the external concentration polarization, the internal concentration polarization and the hydrophilicity, and outputs an internal concentration-external concentration adaptation degree; the flow state evaluation module outputs an evaluation coefficient according to the flow velocity, the heating temperature and the turbulence intensity; and the pumping power adjusting module constructs a model according to the rated power, the flow state evaluation coefficient and the internal concentration-external concentration adaptation degree to output target power. According to the method, the pumping power is adjusted by applying the system, and the problem that accurate prediction and timely regulation and control of the separation efficiency are difficult to realize by a traditional membrane separation technology is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-salt wastewater treatment, and in particular relates to a high-salt wastewater treatment process system and method. Background Art

[0002] High-salinity wastewater, originating from industries such as coal chemical, pharmaceuticals, pesticides, petroleum refining, and seawater utilization, is highly corrosive, complex in composition, and difficult to biodegrade. Direct discharge of this wastewater can lead to serious environmental problems such as soil degradation, eutrophication, and salinization. Traditional biochemical treatment methods are significantly less effective in high-salinity environments because high osmotic pressure dehydrates microbial cells, impairs cell membrane function, and reduces enzyme efficiency, making it difficult to effectively degrade organic matter.

[0003] Current treatment technologies for high-salinity wastewater primarily include pretreatment, membrane concentration, evaporation and crystallization, and resource recovery. Pretreatment typically involves chemical softening (e.g., adding lime or soda ash to remove calcium and magnesium ions) and advanced oxidation (e.g., catalytic ozone oxidation) to remove suspended solids, hardness, and some organic matter. Membrane concentration technologies such as disc-tube reverse osmosis (DTRO), electrodialysis (ED / EDR), and nanofiltration (NF) are used for desalination and concentration reduction. DTRO can treat wastewater with a TDS of up to 180,000 mg / L. Evaporation and crystallization technologies such as mechanical vapor recompression (MVR) and multiple-effect evaporation (MED) serve as terminal treatment, achieving salt solidification and resource recovery. Furthermore, new technologies such as wet oxidation (WAO), fractional crystallization, and bipolar membrane electrodialysis (BMED) are being explored for organic degradation and salt resource recovery.

[0004] Despite the diversity of existing technologies, there are still many defects: First, the membrane separation process is susceptible to pollution and scaling, and traditional technologies make it difficult to monitor and adjust operating parameters in real time to deal with pollution; second, the existing system lacks comprehensive evaluation and coordinated regulation of wastewater status (such as osmotic pressure, pH, viscosity), pollutant characteristics (organic matter, inorganic matter, particulate matter concentration) and membrane material status (external / internal concentration polarization, hydrophilicity); third, evaporation and crystallization energy consumption is extremely high, and resource recovery efficiency is low; finally, existing processes mostly rely on empirical operations, making it difficult to accurately predict separation efficiency and timely adjust pumping power, resulting in unstable system operation and poor economy. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a high-salt wastewater treatment process system and method, aiming to solve the problem that traditional membrane separation technology is difficult to achieve accurate prediction and timely regulation of separation efficiency.

[0006] The present invention is achieved by providing a high-salt wastewater treatment process system, comprising: The wastewater status assessment module builds a wastewater status assessment model based on the osmotic pressure, pH and viscosity of the wastewater and outputs the wastewater status assessment coefficient; The pollutant state assessment module builds a pollutant state assessment model based on the concentration of organic matter, inorganic matter and particulate matter in the wastewater, and outputs the pollutant state assessment coefficient; The internal concentration difference-external concentration difference adaptation evaluation module builds an internal concentration difference-external concentration difference adaptation model based on the wastewater state evaluation coefficient, the pollutant state evaluation coefficient, and the external concentration polarization and internal concentration polarization under the hydrophilicity of the membrane material to output the internal concentration difference-external concentration difference adaptation degree; The flow state assessment module builds a flow state assessment model based on the wastewater flow rate, heating temperature and turbulence intensity, and outputs the flow state assessment coefficient; The pumping power regulation module builds a pumping power regulation model based on the rated pumping power, flow state evaluation coefficient, and internal concentration difference-external concentration difference adaptability, and outputs the target power.

[0007] A further technical solution is to use the maximum and minimum normalization formula to process the osmotic pressure value and viscosity value of the wastewater respectively, thereby obtaining the osmotic pressure index and viscosity index respectively; the actual pH value of the wastewater is subtracted from 7, the absolute value is taken and then divided by 6 to obtain the pH index; The wastewater status assessment model is: ; in 、 as well as are the osmotic pressure weight coefficient, pH weight coefficient and viscosity weight coefficient, respectively, and , 、 as well as Both greater than ; is the osmotic pressure index, is the pH index, is the viscosity index, is the wastewater status assessment coefficient.

[0008] A further technical solution is to use the maximum normalization formula to process the organic matter concentration value, inorganic matter concentration value and particulate matter concentration value in the wastewater respectively, thereby obtaining the organic matter concentration index, inorganic matter concentration index and particulate matter concentration index respectively; The pollutant status assessment model is: ; in 、 as well as are the organic matter concentration weight coefficient, inorganic matter concentration weight coefficient and particulate matter concentration weight coefficient, respectively, and , 、 as well as Both greater than ; Indicates the organic matter concentration index, represents the inorganic matter concentration index, represents the particle concentration index, is the pollutant status assessment coefficient.

[0009] A further technical solution is to use a maximum normalization formula to process the external concentration polarization value and the internal concentration polarization value near the membrane material, thereby obtaining the external concentration polarization index and the internal concentration polarization index respectively; the hydrophilicity of the membrane material represents the affinity of the membrane surface to water, and the hydrophilicity of the membrane material is measured by the contact angle θ. The contact angles are processed using the maximum and minimum normalization formulas to obtain the hydrophilicity index of the membrane material; The membrane material status assessment model is: The internal concentration difference-external concentration difference adaptation model is: ; in 、 、 as well as They are the wastewater state weight coefficient, pollutant state weight coefficient, internal and external concentration extreme difference weight coefficient, and membrane material hydrophilicity weight coefficient, and , 、 、 as well as Both greater than ; is the external concentration polarization index, is the internal concentration polarization index, is the hydrophilic index of the membrane material, is the internal concentration difference-external concentration difference adaptation, is the wastewater status assessment coefficient, is the pollutant status assessment coefficient.

[0010] A further technical solution is to process the flow velocity value of the wastewater using a maximum normalization formula to obtain a flow velocity index; to take the absolute value of the difference between the actual temperature of the wastewater and the optimal temperature (obtained based on production experience or experimental data), and then divide it by the actual maximum temperature difference of the wastewater to obtain a temperature index; and to process the turbulence intensity of the wastewater using a maximum normalization formula to obtain a turbulence intensity index. The flow state assessment model is: ; in 、 as well as are velocity weight coefficient, temperature weight coefficient and turbulence intensity weight coefficient respectively, and , 、 as well as Both greater than ; is the flow velocity index, is the temperature index, is the turbulence intensity index, is the flow state evaluation coefficient.

[0011] Further technical solutions, the pumping power regulation model is: ; in is the rated pumping power, is the flow state evaluation coefficient, is the flow compensation coefficient, is the efficiency compensation coefficient, is the target power, is the internal concentration difference-external concentration difference adaptation.

[0012] A high-salt wastewater treatment method is based on the above-mentioned high-salt wastewater treatment process system, and applies the above-mentioned high-salt wastewater treatment process system to adjust the pumping power of the wastewater.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention constructs a multi-parameter comprehensive evaluation model (such as the internal-external concentration difference adaptation model and the pumping power regulation model) by synergizing the wastewater state assessment module, the pollutant state assessment module, the internal-external concentration difference adaptation assessment module, and the flow state assessment module. This enables real-time and precise regulation of the pumping power, improving the system's adaptability and stability. 2. The present invention dynamically adjusts pumping power based on the internal-external concentration difference compatibility by outputting wastewater state assessment coefficients, pollutant state assessment coefficients, and flow state assessment coefficients. This reduces the risk of membrane fouling and scaling, improves separation efficiency, and reduces energy consumption, helping to address the industry bottleneck of high energy consumption in high-salinity wastewater treatment. 3. Through precise control, the system reduces reagent consumption and downtime, improves water recovery rate and salt resource purity (such as industrial salt recovery). At the same time, the application of intelligent models reduces operating costs, promoting the transformation of high-salinity wastewater treatment from a "cost center" to a "value center"; 4. The present invention organically combines pretreatment, membrane concentration, evaporation crystallization and other unit processes through an intelligent evaluation module, achieving process integration and high-end equipment, providing a replicable new "pollution reduction and carbon reduction" model for high-salt wastewater treatment, and has broad industrial application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the principle of the high-salt wastewater treatment process system of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0017] A high-salt wastewater treatment process system provided in one embodiment of the present invention includes: The wastewater status assessment module builds a wastewater status assessment model based on the osmotic pressure, pH and viscosity of the wastewater and outputs the wastewater status assessment coefficient; The pollutant state assessment module builds a pollutant state assessment model based on the concentration of organic matter, inorganic matter and particulate matter in the wastewater, and outputs the pollutant state assessment coefficient; The internal concentration difference-external concentration difference adaptation evaluation module builds an internal concentration difference-external concentration difference adaptation model based on the wastewater state evaluation coefficient, the pollutant state evaluation coefficient, and the external concentration polarization and internal concentration polarization under the hydrophilicity of the membrane material to output the internal concentration difference-external concentration difference adaptation degree; The flow state assessment module builds a flow state assessment model based on the wastewater flow rate, heating temperature and turbulence intensity, and outputs the flow state assessment coefficient; The pumping power regulation module builds a pumping power regulation model based on the rated pumping power, flow state evaluation coefficient, and internal concentration difference-external concentration difference adaptability, and outputs the target power.

[0018] Specifically, after collecting the osmotic pressure data, the wastewater state evaluation module generates an osmotic pressure index through normalization processing, and combines the pH offset and the viscosity index to perform weighted calculation and output an evaluation coefficient reflecting the physicochemical properties of the wastewater. The pollutant state evaluation module synchronously acquires the concentration data of the three types of pollutants, eliminates the dimensional difference through maximum value normalization, and generates a pollution risk coefficient according to a preset weight. The concentration difference-in-concentration difference adaptation degree evaluation module monitors the concentration difference polarization degree and the contact angle data in real time, combines the wastewater and pollutant evaluation coefficients, and constructs an adaptation degree model to quantify the matching degree of the membrane material and the current working condition. The flow state evaluation module collects the flow rate, temperature and turbulence intensity data, and generates a flow state coefficient after standardization processing. The pumping power adjustment module receives the adaptation degree and the flow coefficient, dynamically corrects the rated power through a nonlinear compensation model, automatically increases the power to compensate for the loss of membrane flux when the adaptation degree is detected to be reduced, and reduces the power to reduce energy consumption when the flow state is improved.

[0019] Compared with the prior art, the traditional method uses independent sensors to monitor a single parameter, and the present scheme constructs an evaluation system through multi-source data fusion. The existing control system only adjusts the pump speed according to the preset flow rate, and the present scheme introduces an adaptation degree parameter to dynamically reflect the membrane pollution risk, and realizes double compensation adjustment in combination with the flow state. In the conventional process, the concentration difference polarization monitoring and the pumping control belong to independent subsystems, and the present scheme realizes cross-module data interaction by establishing an adaptation degree model, and directly associates the membrane material state with the power adjustment decision.

[0020] Through the above technical scheme, the present application effectively suppresses the membrane flux attenuation caused by the aggravation of concentration difference polarization, and maintains a stable transmembrane pressure difference through dynamic power compensation. The multi-dimensional evaluation system early warns of the pollutant deposition risk and reduces the frequency of chemical cleaning. The coordinated regulation of the flow state and the adaptation degree makes the pumping power always match the current working condition demand, delays the performance degradation in the initial stage of membrane pollution by increasing the power, and reduces the power to realize energy-saving operation when the turbulence intensity is enhanced.

[0021] As a preferred embodiment of the present application, the maximum and minimum normalization formula is used to process the osmotic pressure value and the viscosity value of the wastewater respectively, so as to obtain the osmotic pressure index and the viscosity index respectively; the difference between the actual pH value of the wastewater and 7 is taken as an absolute value and then divided by 6 to obtain the pH index; The wastewater state evaluation model is: ; Among them , and are the osmotic pressure weight coefficient, the pH weight coefficient and the viscosity weight coefficient respectively, and , , and are all greater than ; is the osmotic pressure index, is the pH index, is the viscosity index, is the wastewater status assessment coefficient.

[0022] Among them, the maximum and minimum normalization formula refers to mapping the original parameter values ​​to the [0,1] interval through linear transformation. This processing method is used to eliminate the dimensional differences of osmotic pressure and viscosity parameters and make parameters of different magnitudes comparable.

[0023] Among them, the pH index refers to the degree of deviation between the actual pH value and the neutral value, which is used to quantify the impact of the acid-base properties of the solution on the performance of the membrane material.

[0024] Among them, the osmotic pressure weight coefficient , pH weight coefficient and viscosity weight coefficient Parameters used to adjust the contribution of osmotic pressure, pH and viscosity to the evaluation results can be set by fitting experimental data or expert experience, for example, 、 、 ,This parameter setting enables the model to dynamically adjust the priority of each parameter according to the actual working conditions.

[0025] Specifically, the osmotic pressure and viscosity parameters are first normalized by the maximum and minimum values. For example, when the measured osmotic pressure value is 1200 kPa, if the historical maximum value is 1500 kPa and the minimum value is 800 kPa, the calculated value is =0.571. The pH index is calculated by calculating the absolute deviation of the actual pH from the neutral value and dividing it by the maximum possible deviation range, for example, when pH=9, = 0.333. The viscosity index uses the same normalization method. For example, when the viscosity is 1.2 mPas, if the maximum value is 2.0 mPas and the minimum value is 0.8 mPas, we get =0.333. Finally, the wastewater status assessment coefficient is obtained through weighted summation. This coefficient comprehensively reflects the comprehensive impact of wastewater on the membrane separation process.

[0026] Compared with the existing technology, the traditional method directly uses the original values ​​of osmotic pressure, pH and viscosity for superposition calculation, which leads to distortion of the evaluation results due to the difference in parameter dimensions. For example, the unit of osmotic pressure is kPa and the viscosity is mPas. Direct addition will amplify the numerical influence of osmotic pressure. The present application unifies the parameters into dimensionless indices through normalization processing, such as mapping the osmotic pressure from 800-1500 kPa to the range of 0-1, and mapping the viscosity from 0.8-2.0 mPas to the range of 0-1, eliminating the interference of dimensional differences on the evaluation results. At the same time, the existing technology does not take into account the nonlinear effect of pH deviation from the neutral value, such as the difference in effect of pH=9 and pH=5 on membrane fouling, while the present application quantifies the degree of pH deviation into a linear indicator through standardized calculation, which is more in line with the influence of acid-base imbalance on membrane materials in actual working conditions.

[0027] Through the above technical solution, this application solves the evaluation error problem caused by the difference in parameter dimensions in the traditional method. The osmotic pressure, viscosity and pH index are made comparable through normalization. For example, under the working conditions of osmotic pressure of 1200 kPa, viscosity of 1.2 mPas and pH=9, the original parameters cannot be directly superimposed. After standardization, the indexes are 0.571, 0.333 and 0.333 respectively, which can accurately reflect the relative influence of each parameter. At the same time, the osmotic pressure weight coefficient , pH weight coefficient and viscosity weight coefficient The introduction of α allows the model to adjust parameter priorities according to membrane material type or fouling risk, such as increasing the α in membrane systems susceptible to osmotic pressure. weights, thereby optimizing the accuracy of pumping power regulation.

[0028] As a preferred embodiment of the present invention, the organic matter concentration value, the inorganic matter concentration value and the particulate matter concentration value in the wastewater are processed respectively using the maximum normalization formula, thereby obtaining the organic matter concentration index, the inorganic matter concentration index and the particulate matter concentration index respectively; The pollutant status assessment model is: ; in 、 as well as are the organic matter concentration weight coefficient, inorganic matter concentration weight coefficient and particulate matter concentration weight coefficient, respectively, and , 、 as well as Both greater than ; To express the organic matter concentration index, represents the inorganic matter concentration index, represents the particle concentration index, is the pollutant status assessment coefficient.

[0029] The maximum normalization formula involves dividing each pollutant concentration by its corresponding historical maximum or theoretical threshold, mapping the concentration data to a range of 0-1, eliminating dimensional differences and making the concentrations of different pollutants comparable. Specifically, this can be achieved by dividing the real-time collected organic matter concentration by the preset upper limit of the organic matter concentration. Organic matter concentration can be measured using an online COD monitor, inorganic matter concentration can be indirectly reflected by a conductivity / TDS sensor, and particulate matter concentration can be monitored online using a turbidity sensor or laser particle counter. For example, when the organic matter concentration is 500 mg / L and the preset upper limit is 1000 mg / L, an organic matter concentration index of 0.5 is calculated.

[0030] Among them, the organic matter concentration weight coefficient , inorganic concentration weight coefficient and the particle concentration weight coefficient It refers to the proportional parameter that reflects the contribution of different pollutants to the overall pollution state. 、 as well as The sum is 1 and 、 as well as Are all greater than 0, ensuring that the assessment results include the synergistic effects of multiple pollutants while avoiding the dominance of a single pollutant. Specifically, the weights can be determined by expert experience or principal component analysis. For example, when organic pollution is the main contradiction, It can be 0.5, and Take 0.25 each.

[0031] Specifically, the original concentration data of organic matter, inorganic matter and particulate matter are obtained through real-time monitoring, and the maximum value normalization process is used to eliminate the dimension difference and generate a standardized index within the range of 0-1. The three types of indexes are further linearly superimposed according to the preset weights to dynamically characterize the complexity of the pollutant composition. For example, when the concentration of particulate matter suddenly increases and causes When the index reaches 0.8, if its weight If the value is 0.3, it directly contributes 0.24 to the pollutant status assessment coefficient, triggering the coordinated adjustment of subsequent treatment parameters. By quantifying the comprehensive impact of pollutants, this model provides a data basis for membrane fouling prediction and pumping power optimization.

[0032] Compared with existing technologies, traditional methods only monitor single pollutant indicators or use simple summation calculations, failing to distinguish the differential impacts of different pollutants on membrane fouling. This solution eliminates dimensional interference through normalization and emphasizes the role of key pollutants through weight allocation, addressing the one-sidedness of traditional assessment methods.

[0033] Through the above technical solution, this application achieves a dynamic and comprehensive assessment of the concentrations of multiple pollutants, accurately identifying the main pollution sources and providing a quantitative basis for predicting reverse osmosis membrane cleaning cycles and regulating pumping pressure. For example, when the inorganic concentration index is continuously above 0.7, the anti-scaling agent dosing system can be activated in advance to avoid flux reduction caused by salt crystallization on the membrane surface.

[0034] As a preferred embodiment of the present invention, the external concentration polarization value and the internal concentration polarization value near the membrane material are processed using a maximum normalization formula to obtain an external concentration polarization index and an internal concentration polarization index, respectively. The hydrophilicity of the membrane material represents the affinity of the membrane surface for water. The hydrophilicity of the membrane material is measured by the contact angle θ. The contact angles are processed using the maximum and minimum normalization formulas to obtain the hydrophilicity index of the membrane material. The internal concentration difference-external concentration difference adaptation model is: ; in 、 、 as well as They are the wastewater state weight coefficient, pollutant state weight coefficient, internal and external concentration extreme difference weight coefficient, and membrane material hydrophilicity weight coefficient, and , 、 、 as well as Both greater than ; is the external concentration polarization index, is the internal concentration polarization index, is the hydrophilic index of the membrane material, is the internal concentration difference-external concentration difference adaptation, is the wastewater status assessment coefficient, is the pollutant status assessment coefficient.

[0035] Among them, the external concentration polarization index refers to the boundary layer effect parameter formed by normalizing the solute concentration gradient on the membrane surface with the maximum value. The ion concentration gradient can be measured by a microelectrode array near the membrane surface. Specifically, it can be achieved by the ratio of the current polarization value to the historical maximum polarization value, which is used to quantify the influence of membrane surface fluid dynamics on mass transfer resistance.

[0036] The internal concentration polarization index refers to the solute concentration gradient parameters inside the membrane pores normalized by the maximum value. It is often indirectly inferred through electrochemical impedance spectroscopy or membrane flux attenuation model. Specifically, it can be achieved by the ratio of the current polarization value to the maximum polarization value allowed by the membrane pore structure, and is used to characterize the change in the internal permeation resistance of the membrane.

[0037] The hydrophilic index of membrane materials refers to the parameter obtained by normalizing the contact angle θ to its maximum and minimum values. Specifically, it can be achieved by using the ratio of the difference between the measured value of θ and the theoretical extreme value of the contact angle. The measured value of θ can be indirectly evaluated by a surface energy analyzer to reflect the anti-fouling ability of the membrane surface.

[0038] Wastewater state weight coefficient , pollutant state weight coefficient , weight coefficient of internal and external concentration extreme difference and the hydrophilicity weight coefficient of the membrane material It refers to the parameters used to adjust the influence of wastewater state, pollutant state, concentration polarization effect and hydrophilicity on fitness. Specifically, the contribution weight of each factor can be determined by the hierarchical analysis method or the empirical calibration method.

[0039] Specifically, after the external concentration polarization value and the internal concentration polarization value are normalized to their maximum values, the dimension difference of the polarization degree under different working conditions is eliminated, so that and The index can compare the working conditions of different membrane components horizontally. The contact angle θ is converted into a hydrophilic index H in the range of 0-1 through maximum and minimum normalization. When the contact angle approaches 0 degrees, H approaches 1, indicating that the membrane material has super hydrophilic properties. The internal concentration difference-external concentration difference adaptation model is calculated by converting the wastewater state coefficient , pollutant coefficient and 、 , H are weightedly combined to construct a dynamic equilibrium relationship. The parameters in the denominator of the model correspond to the comprehensive effects of wastewater physicochemical characteristics, pollutant composition, concentration polarization intensity and membrane hydrophilicity. to The distribution ratio determines the sensitivity of each factor to the adaptability. When the osmotic pressure of wastewater increases or the concentration of pollutants increases, and The increase in fitness will lead to Reduced, at this time it is necessary to improve the flow state by adjusting the pumping power to restore the adaptability.

[0040] Compared with existing technologies, traditional membrane separation technology only monitors a single concentration polarization indicator and lacks a correlation model with wastewater status, making it impossible to predict membrane fouling trends. Existing methods typically use fixed operating parameters to address concentration polarization and lack a response mechanism to dynamic changes in membrane hydrophilicity. This solution constructs a multi-parameter coupled fitness model to achieve coordinated regulation of external and internal concentration polarization, while also incorporating membrane material hydrophilicity into a real-time evaluation system. This overcomes the technical flaw of traditional technologies that disconnects membrane status from water quality conditions.

[0041] Through the above technical solution, the present application can dynamically calculate the fitness index based on the real-time monitoring of wastewater osmotic pressure, pollutant concentration and membrane material status. When the fitness is detected to be lower than the threshold, the pumping power adjustment is automatically triggered, effectively suppressing the membrane flux attenuation caused by concentration polarization imbalance. This solution quantifies the compensatory effect of the hydrophilicity of the membrane material on concentration polarization, automatically enhancing the turbulence intensity when the contact angle of the membrane surface increases, thereby maintaining a stable separation efficiency. In addition, 、 、 as well as The adjustable characteristics enable the model to adapt to the differences in the components of high-salt wastewater in different industries and improve the generalization ability of the process system.

[0042] In a preferred embodiment of the present invention, the flow velocity value of the wastewater is processed using a maximum normalization formula to obtain a flow velocity index; the absolute value of the difference between the actual temperature of the wastewater and the optimal temperature (obtained based on production experience or experimental data) is taken and then divided by the actual maximum temperature difference of the wastewater to obtain a temperature index; the turbulence intensity of the wastewater is processed using a maximum normalization formula to obtain a turbulence intensity index; The flow state assessment model is: ; in 、 as well as are velocity weight coefficient, temperature weight coefficient and turbulence intensity weight coefficient respectively, and , 、 as well as Both greater than ; is the flow velocity index, is the temperature index, is the turbulence intensity index, is the flow state evaluation coefficient.

[0043] The flow rate index is obtained by dividing the original flow rate data by the maximum flow rate value allowed by the system, which maps the flow rate value to the interval of 0 to 1. The flow rate data can be measured by an ultrasonic Doppler flowmeter, and the maximum flow rate threshold can be determined by real-time acquisition of flow rate data and division by a preset maximum flow rate threshold. The temperature index is obtained by calculating the absolute deviation of the actual temperature from the preset optimal temperature, and then dividing by the maximum temperature difference range that can occur during system operation. The temperature index can be calculated using real-time monitoring data from a temperature sensor and combining a preset optimal temperature parameter. The temperature index quantifies the influence of temperature fluctuations on membrane flux. The turbulence intensity index is obtained by dividing the actual turbulence intensity data by the maximum turbulence intensity value allowed by the system. Turbulence intensity is the ratio of flow rate fluctuation standard deviation to average flow rate. The flow rate can be measured by an ultrasonic Doppler flowmeter. The actual turbulence intensity can be calculated using the above formula. The maximum turbulence intensity threshold can be determined using a fluid mechanics simulation model or actual measurement data. The turbulence intensity index represents the inhibitory effect of turbulence on concentration polarization. The temperature weight coefficient The turbulence intensity weight coefficient is a pre-set parameter that reflects the contribution of flow rate, temperature, and turbulence intensity to the flow state. The parameter can be determined by historical data regression analysis or expert experience assignment. It is used to dynamically balance the comprehensive influence of different parameters on the flow state.

[0044] Specifically, the maximum flow rate is normalized to convert the flow rate data of different dimensions into dimensionless indices, ensuring that the influence of flow rate on membrane surface shear force can be quantitatively compared. The temperature index standardizes the deviation of actual temperature from optimal temperature, avoiding the effects of excessive temperature on membrane material thermal expansion or the effects of low temperature on the decrease in solubility of pollutants. The normalization of turbulence intensity accurately reflects the ability of fluid turbulence to destroy the concentration polarization layer. After inputting the above three indices into the linear weighting model, the contribution proportion of each parameter is dynamically adjusted according to the pre-set flow rate weight coefficient , temperature weight coefficient , and turbulence intensity weight coefficient The final output of the flow state evaluation coefficient can real-time represent the influence of the current flow state on membrane fouling and pumping efficiency, providing a quantitative basis for subsequent power regulation.

[0045] Compared with the prior art, the traditional method only adjusts the pumping power based on fixed flow rate or empirical temperature parameters, without considering the dynamic influence of turbulence intensity on concentration polarization, and lacks comprehensive evaluation of the coupling of multiple parameters. The present scheme constructs a normalization index system including flow rate, temperature, and turbulence intensity, and realizes dynamic quantitative evaluation of flow state by combining a weighting model, overcoming the defects of single parameter control lag and one-sidedness.

[0046] Through the above-mentioned technical solution, the present application can monitor the dynamic changes of flow velocity, temperature, and turbulence intensity in real time, accurately quantify the impact of flow conditions on membrane fouling and pumping efficiency, and provide real-time feedback parameters for pumping power regulation. This solution effectively solves the problem of increased membrane fouling caused by incomplete flow state monitoring in traditional technologies, while also avoiding energy waste caused by delayed pumping power regulation, achieving a dynamic balance between treatment system operating efficiency and energy consumption control.

[0047] As a preferred embodiment of the present invention, the pumping power regulation model is: ; in is the rated pumping power, is the flow state evaluation coefficient, is the flow compensation coefficient, is the efficiency compensation coefficient, is the target power, is the internal concentration difference-external concentration difference adaptation.

[0048] Among them, the rated pumping power refers to the designed pumping power of the system under standard operating conditions, which can be achieved by using equipment nameplate parameters or experimental calibration values ​​as a benchmark reference value for power regulation. The flow state assessment coefficient refers to a comprehensive indicator that reflects the influence of wastewater flow rate, temperature and turbulence intensity on flow resistance. It can be obtained by weighted calculation after normalizing the flow rate, temperature deviation and turbulence intensity parameters, and is used to quantify the demand for pumping power due to the flow state. The flow compensation coefficient refers to the power adjustment parameter used to correct the flow state when it deviates from the ideal operating conditions. It can be dynamically optimized using empirical values ​​or adaptive algorithms to reduce additional energy consumption caused by insufficient flow rate or abnormal temperature. The efficiency compensation coefficient refers to an adjustment parameter used to balance the risk of membrane fouling and energy consumption. It can be determined by empirical values ​​or the results of membrane material status assessment. It is used to increase the power when the risk of membrane fouling is high to enhance the membrane surface flushing effect. The internal concentration-external concentration compatibility refers to an indicator that characterizes the degree of matching between the concentration polarization near the membrane material and the wastewater state and pollutant state. It can be calculated by normalizing the external concentration polarization, internal concentration polarization and membrane hydrophilicity parameters and then constructing a mathematical model to evaluate the potential risk of membrane fouling.

[0049] Specifically, the model achieves real-time optimization of pumping power by dynamically linking the rated pumping power with the flow state evaluation coefficient and the internal concentration difference-external concentration difference fit. When the flow state evaluation coefficient increases, it indicates that the wastewater flow rate decreases, the temperature deviates, or the turbulence intensity is insufficient. At this time, the pumping power is reduced through the flow compensation coefficient to avoid ineffective energy consumption. When the internal concentration difference-external concentration difference fit decreases, it indicates that the risk of membrane fouling increases. At this time, the pumping power is increased through the efficiency compensation coefficient to strengthen the shear force on the membrane surface and inhibit pollutant deposition. The product structure in the formula enables the flow state optimization and membrane fouling suppression to form a synergistic effect. For example, when the flow state deteriorates and the fit decreases, the power adjustment amount achieves a more significant adjustment range through the superposition of two compensations, thereby maintaining the system operating efficiency under complex working conditions.

[0050] Compared to existing technologies, traditional methods are typically based on fixed pumping power or single parameter threshold adjustment, and are unable to simultaneously respond to changes in flow state and membrane fouling risks. For example, existing technologies only perform linear power adjustment based on flow rate or pressure sensor signals, ignoring the impact of temperature fluctuations on viscosity and the constraints of concentration polarization on membrane flux. However, this solution, by introducing a dynamic coupling of the flow state assessment coefficient and the internal-external concentration difference adaptation, can more comprehensively capture the interaction between flow resistance and membrane fouling factors in the wastewater treatment process, thereby achieving adaptive and precise control of pumping power.

[0051] Through the above technical solution, this application solves the problem of insufficient dynamic matching between pumping power regulation and operating conditions in high-salinity wastewater treatment, effectively reducing energy waste caused by deteriorating flow conditions. It also extends the service life of membrane materials through real-time feedback regulation of membrane fouling risks. This model automatically balances energy consumption and separation efficiency under varying salinity, pollutant concentration, and temperature conditions, avoiding the lag and uncertainty associated with manual experience, significantly improving the stability and economic efficiency of system operation.

[0052] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A high-salt wastewater treatment process system, characterized in that: include: The wastewater status assessment module builds a wastewater status assessment model based on the osmotic pressure, pH and viscosity of the wastewater and outputs the wastewater status assessment coefficient; The pollutant state assessment module builds a pollutant state assessment model based on the concentration of organic matter, inorganic matter and particulate matter in the wastewater, and outputs the pollutant state assessment coefficient; The internal concentration difference-external concentration difference adaptation evaluation module builds an internal concentration difference-external concentration difference adaptation model based on the wastewater state evaluation coefficient, the pollutant state evaluation coefficient, and the external concentration polarization and internal concentration polarization under the hydrophilicity of the membrane material to output the internal concentration difference-external concentration difference adaptation degree; The flow state assessment module builds a flow state assessment model based on the wastewater flow rate, heating temperature and turbulence intensity, and outputs the flow state assessment coefficient; The pumping power regulation module builds a pumping power regulation model based on the rated pumping power, flow state evaluation coefficient, and internal concentration difference-external concentration difference adaptability, and outputs the target power.

2. The high-salt wastewater treatment process system according to claim 1, characterized in that: The osmotic pressure value and viscosity value of the wastewater are processed using the maximum and minimum normalization formula to obtain the osmotic pressure index and viscosity index respectively; the actual pH value of the wastewater is subtracted from 7, the absolute value is taken and then divided by 6 to obtain the pH index; The wastewater status assessment model is: ； in 、 as well as are the osmotic pressure weight coefficient, pH weight coefficient and viscosity weight coefficient, respectively, and , 、 as well as All greater than 0; is the osmotic pressure index, is the pH index, is the viscosity index, is the wastewater status assessment coefficient.

3. The high-salt wastewater treatment process system according to claim 1, characterized in that: The maximum normalization formula is used to process the organic matter concentration value, inorganic matter concentration value and particulate matter concentration value in the wastewater respectively, thereby obtaining the organic matter concentration index, inorganic matter concentration index and particulate matter concentration index respectively; The pollutant status assessment model is: ； in 、 as well as are the organic matter concentration weight coefficient, inorganic matter concentration weight coefficient and particulate matter concentration weight coefficient, respectively, and , 、 as well as All greater than 0; Indicates the organic matter concentration index, represents the inorganic matter concentration index, represents the particle concentration index, is the pollutant status assessment coefficient.

4. The high-salt wastewater treatment process system according to claim 1, characterized in that: The external concentration polarization value and the internal concentration polarization value near the membrane material are processed by the maximum normalization formula to obtain the external concentration polarization index and the internal concentration polarization index respectively. The hydrophilicity of membrane materials refers to the affinity of the membrane surface to water. The hydrophilicity of membrane materials is measured by the contact angle θ. The contact angle is processed using the maximum and minimum normalization formula to obtain the hydrophilic index of the membrane material. The internal concentration difference-external concentration difference adaptation model is: ； in 、 、 as well as They are the wastewater state weight coefficient, pollutant state weight coefficient, internal and external concentration extreme difference weight coefficient, and membrane material hydrophilicity weight coefficient, and , 、 、 as well as Both greater than ; is the external concentration polarization index, is the internal concentration polarization index, is the hydrophilic index of the membrane material, is the internal concentration difference-external concentration difference adaptation, is the wastewater status assessment coefficient, is the pollutant status assessment coefficient.

5. The high-salt wastewater treatment process system according to claim 4, characterized in that: The flow rate value of the wastewater is processed using the maximum normalization formula to obtain the flow rate index; the difference between the actual temperature of the wastewater and the optimal temperature is subtracted, the absolute value is taken and then divided by the actual maximum temperature difference of the wastewater to obtain the temperature index; The turbulence intensity of wastewater is processed using the maximum normalization formula to obtain the turbulence intensity index; The flow state assessment model is: ； in 、 as well as are velocity weight coefficient, temperature weight coefficient and turbulence intensity weight coefficient respectively, and , 、 as well as Both greater than ; is the flow velocity index, is the temperature index, is the turbulence intensity index, is the flow state evaluation coefficient.

6. The high-salt wastewater treatment process system according to claim 5, characterized in that: The pumping power regulation model is: ； in is the rated pumping power, is the flow state evaluation coefficient, is the flow compensation coefficient, is the efficiency compensation coefficient, is the target power, is the internal concentration difference-external concentration difference adaptation.

7. A method for treating high-salt wastewater, based on the high-salt wastewater treatment process system according to any one of claims 1 to 6, characterized in that: The high-salt wastewater treatment process system is used to adjust the pumping power of the wastewater.

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