Cooperative treatment system and method for reverse osmosis concentrated water and municipal sewage

Abstract

The invention relates to the technical field of sewage treatment, and particularly discloses a reverse osmosis concentrated water and urban sewage cooperative treatment system and method.The system comprises a pretreatment module used for collecting and pretreating two water sources and dynamically determining the optimal mixing ratio through a BP neural network and a genetic algorithm; the biochemical treatment module is used for treating the mixed water by adopting an A2O process and monitoring anaerobic, anoxic, aerobic and sludge states in real time so as to realize fine diagnosis and early warning in the biochemical process; the deep treatment module is used for performing deep purification by adopting an ultrafiltration and reverse osmosis double-membrane process, evaluating pollution and scaling risks in real time by analyzing membrane state data, and adaptively optimizing the backwashing frequency and the dosage of a scale inhibitor. According to the invention, intelligent optimization of a mixing ratio, precise monitoring of biochemical treatment and self-adaptive operation of a membrane system are realized, the problems of inhibition of high-salinity concentrated water on biochemical treatment, high membrane pollution and scaling risk, extensive operation regulation and control and the like are effectively solved, efficient utilization of resources is realized, and the quality of effluent is improved.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, specifically to a system and method for the synergistic treatment of reverse osmosis concentrate and urban wastewater. Background Technology

[0002] Reverse osmosis (RO) technology, as a key means of efficient desalination and deep purification, has been widely used in seawater desalination, industrial pure water production, and various wastewater reuse fields. However, while producing product water, the reverse osmosis process inevitably generates a concentrate, accounting for approximately 20%-30% of the total influent, known as reverse osmosis concentrate. This concentrate is characterized by high salinity, high hardness, high alkalinity, and contains trace amounts of recalcitrant organic matter and residual scale inhibitors, exhibiting extremely poor biodegradability. Direct discharge would cause severe osmotic pressure shocks and ecological risks to receiving water bodies, while further desalination treatment is extremely costly, becoming a bottleneck restricting the sustainable development of reverse osmosis technology. On the other hand, urban wastewater treatment is rapidly developing towards upgrading and resource utilization. After secondary biological treatment, the concentrations of pollutants such as organic matter, nitrogen, and phosphorus in the effluent of traditional urban wastewater have been significantly reduced, but it still contains a certain amount of dissolved salts, recalcitrant organic matter, and pathogenic microorganisms, making it difficult to meet increasingly stringent high-quality reuse requirements, especially in scenarios with high desalination rates. Therefore, seeking cost-effective and efficient advanced treatment and desalination technologies is key to realizing the resource utilization of urban wastewater. Co-treatment of reverse osmosis concentrate with urban wastewater is considered a promising solution. Its core concept lies in utilizing the large volume and relatively abundant nutrients (carbon, nitrogen, phosphorus) of urban wastewater to dilute the high salinity and toxicity of the reverse osmosis concentrate, while simultaneously utilizing the mature microbial community within the urban wastewater biological treatment system to degrade some of the organic matter in the concentrate. Finally, with the aid of subsequent advanced treatment units, such as membrane processes, high-quality effluent can be reused. However, in practical engineering applications, this co-treatment process faces the following problems:

[0003] The mixing ratio is difficult to optimize: the mixing ratio of reverse osmosis concentrate and municipal wastewater directly affects the microbial activity and treatment efficiency of subsequent biological treatment units. If the ratio is too high, excessive salinity and hardness in the mixed liquor will inhibit microbial metabolism, leading to decreased nitrification, denitrification, and biological phosphorus removal efficiency, and even system collapse. If the ratio is too low, the dilution and treatment capacity of municipal wastewater cannot be fully utilized, resulting in limited concentrate treatment volume and low overall resource utilization efficiency. Existing technologies mostly rely on fixed empirical ratios or simple water quality parameters for rough adjustments, lacking a dynamic optimization mechanism that considers multiple constraints such as salinity, scaling tendency, pH, nutrient balance, and microbial activity. This makes it difficult to adapt to fluctuations in the influent water quality and achieve a comprehensive optimization of treatment costs, efficiency, and operational risks.

[0004] Lagging monitoring and diagnosis of biological treatment processes: The operating status of biological units in co-treatment processes is complex. Traditional monitoring methods often focus on the final effluent quality, lacking refined real-time assessment and early warning of the operating status of key sub-processes such as anaerobic phosphorus release, anoxic denitrification, and aerobic nitrification. When effluent quality fails to meet standards, it is difficult to quickly and accurately pinpoint the root cause of the problem, such as anaerobic environment destruction, low denitrification efficiency in the anoxic tank, hindered nitrification in the aerobic tank, or sludge deactivation. This results in delayed and untargeted control measures, affecting the stable operation of the system.

[0005] Advanced membrane treatment systems face high operational risks and require extensive regulation: To achieve high-quality reuse, biological effluent typically requires advanced treatment via an ultrafiltration-reverse osmosis (UFO) dual-membrane process. However, biological effluent may still contain colloids, microbial flocs, and high concentrations of scaling ions, easily leading to UF membrane fouling and RO membrane scaling. Existing operating models often employ fixed backwash frequencies and antiscalant dosages, failing to adapt to actual membrane fouling and scaling risks. Fixed frequencies may result in insufficient or excessive backwashing; fixed antiscalant dosages may lead to insufficient or excessive antiscaling due to water quality fluctuations. The lack of intelligent control strategies based on real-time membrane condition assessment is a major obstacle to the long-term stable and low-consumption operation of dual-membrane systems. Summary of the Invention

[0006] The purpose of this invention is to provide a system and method for the synergistic treatment of reverse osmosis concentrate and urban wastewater, in order to solve the problems mentioned in the background.

[0007] The objective of this invention can be achieved through the following technical solution: The first aspect of this invention provides a system for the synergistic treatment of reverse osmosis concentrate and municipal wastewater, the system comprising:

[0008] The pretreatment module is used to collect reverse osmosis concentrate and municipal wastewater and pretreat them, monitor the water quality data of the pretreated reverse osmosis concentrate and municipal wastewater, determine the optimal mixing ratio of reverse osmosis concentrate and municipal wastewater, and perform corresponding control operations based on the optimal mixing ratio.

[0009] Biochemical processing module, used to employ A 2 The O process performs biological treatment on the mixed water after it has been adjusted to the optimal mixing ratio to obtain biological effluent; at the same time, it monitors the biological treatment information to determine whether the biological treatment is qualified. When the judgment result is unqualified, it identifies the cause of the current unqualified biological treatment and provides corresponding early warning feedback.

[0010] The deep treatment module is used to deeply purify and desalinate biological effluent using a dual-membrane process of ultrafiltration and reverse osmosis, producing high-quality reclaimed water and realizing the resource utilization of concentrated water; at the same time, it monitors the current UF membrane status data and RO membrane status data, assesses the risk of UF membrane fouling and RO membrane scaling in real time, and adaptively optimizes the backwashing frequency and scale inhibitor dosage.

[0011] A second aspect of the present invention provides a method for the synergistic treatment of reverse osmosis concentrate and municipal wastewater, the method comprising the following steps:

[0012] S1. Pretreatment: Collect reverse osmosis concentrate and municipal wastewater and pretreat them. Monitor the water quality data of the pretreated reverse osmosis concentrate and municipal wastewater to determine the optimal mixing ratio of reverse osmosis concentrate and municipal wastewater. Based on the optimal mixing ratio, perform corresponding control operations.

[0013] S2, Biochemical treatment: Using A 2 The O process performs biological treatment on the mixed water after it has been adjusted to the optimal mixing ratio to obtain biological effluent; at the same time, it monitors the biological treatment information to determine whether the biological treatment is qualified. When the judgment result is unqualified, it identifies the cause of the current unqualified biological treatment and provides corresponding early warning feedback.

[0014] S3. Advanced Treatment: The ultrafiltration and reverse osmosis dual-membrane process is used to deeply purify and desalinate the biological effluent, producing high-quality reclaimed water and realizing the resource utilization of concentrated water; at the same time, the current UF membrane status data and RO membrane status data are monitored to assess the UF membrane fouling risk and RO membrane scaling risk in real time, and adaptively optimize the backwashing frequency and scale inhibitor dosage.

[0015] The beneficial effects of this invention are:

[0016] This invention designs differentiated pretreatment processes based on the characteristics and impurity types of reverse osmosis concentrate and municipal sewage. This effectively removes impurities from both sources that pose significant risks to subsequent treatment, while preventing equipment blockage, wear, and siltation in the biological treatment tank, extending equipment lifespan and reducing maintenance frequency and costs. Through multi-constraint design of the mixing ratio, a safe range is defined for subsequent biological treatment from a water quality perspective. A BP neural network, based on historical data, accurately predicts the parameters and overall benefits of the mixed solution under different mixing ratios. Combining multi-objective optimization functions and genetic algorithms, the objective function integrates treatment cost, treatment efficiency, and operational risk, ensuring that the mixing ratio does not merely reduce costs or increase efficiency. The genetic algorithm, under the premise of satisfying constraints, selects the optimal ratio that balances these three factors, avoiding the fixed ratios of traditional experience and achieving adaptive operation. This ensures that the selected mixing ratio maximizes the overall effect of synergistic pollutant removal, rather than optimizing a single indicator, and better meets the multi-indicator requirements of actual emission standards.

[0017] This invention is based on A2 When monitoring and analyzing the biochemical treatment of the O process, the monitoring information of the anaerobic tank, anoxic tank, aerobic tank and sludge is used to identify the causes of non-compliance in the biochemical treatment, discover potential problems in a timely manner, improve the pertinence of tracing the source of biochemical treatment problems, accurately locate the deep process causes that lead to non-compliance of the final effluent, and provide a clear decision-making direction for operation and control.

[0018] This invention integrates real-time transmembrane pressure difference, membrane flux, and clogging density index into ultrafiltration membrane state values ​​and constructs an ultrafiltration membrane state curve. Then, based on the ultrafiltration membrane state curve, time-domain analysis and trend monitoring are performed to generate an ultrafiltration membrane fouling index, achieving multi-dimensional assessment from static accumulation and dynamic trends. This makes membrane fouling assessment more comprehensive and in line with the dynamic changes in actual operation. When membrane fouling occurs in the ultrafiltration membrane, a backwashing frequency adjustment analysis model is constructed to achieve dynamic adaptive adjustment of the backwashing frequency. This avoids the problems of excessive backwashing leading to resource waste or insufficient backwashing leading to increased membrane fouling caused by a fixed backwashing frequency.

[0019] This invention constructs variation curves for the Langeryl saturation index and the saturation of sparingly soluble salts, and then analyzes the fluctuations of the Langeryl saturation index and the saturation of sparingly soluble salts based on the peak and valley characteristics of the curves. This enables a comprehensive assessment of various scaling risks, such as calcium carbonate scale and sulfate scale, overcoming the limitations of traditional methods that rely solely on calcium carbonate scaling tendency. It avoids misjudgments or omissions caused by incomplete monitoring, significantly improving the completeness and reliability of risk assessment. When there is a risk of scaling in the reverse osmosis membrane, a scale inhibitor dosage adjustment analysis model is constructed to achieve dynamic adaptive optimization of scale inhibitor dosage. This avoids the problems of insufficient scale inhibition due to a fixed dosage leading to aggravated membrane scaling or excessive dosage leading to resource waste and increased operating costs. Attached Figure Description

[0020] The invention will now be further described with reference to the accompanying drawings.

[0021] Figure 1 This is an architectural diagram of a system for the coordinated treatment of reverse osmosis concentrate and urban sewage according to the present invention.

[0022] Figure 2 This is an architecture diagram of a preprocessing module of the present invention.

[0023] Figure 3 This is an architectural diagram of a biochemical processing module of the present invention.

[0024] Figure 4 This is an architecture diagram of a deep processing module of the present invention.

[0025] Figure 5 This is a flowchart of a method for the synergistic treatment of reverse osmosis concentrate and urban sewage according to the present invention.

[0026] Figure 6 This is a flow chart of the process for the synergistic treatment of reverse osmosis concentrate and urban sewage according to the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Please see Figure 1 As shown, a system for the co-treatment of reverse osmosis concentrate and municipal wastewater is disclosed. The system includes:

[0029] The pretreatment module 100 is used to collect reverse osmosis concentrate and municipal sewage and pretreat them, monitor the water quality data of the pretreated reverse osmosis concentrate and municipal sewage, determine the optimal mixing ratio of reverse osmosis concentrate and municipal sewage, and perform corresponding control operations based on the optimal mixing ratio.

[0030] Specifically, please refer to Figure 2 As shown, the pretreatment module 100 is equipped with a concentrated water and wastewater collection unit 101, a pretreatment unit 102, an optimal mixing ratio determination unit 103, and an intelligent control unit 104.

[0031] The concentrate and wastewater collection unit 101 is used to simultaneously collect concentrate and municipal wastewater from the reverse osmosis system.

[0032] It should be noted that the concentrate from the reverse osmosis system is collected through a reverse osmosis concentrate collection device, while municipal wastewater is collected through a municipal wastewater collection device.

[0033] Pretreatment unit 102 is used to pretreat reverse osmosis concentrate and municipal wastewater separately to obtain pretreated reverse osmosis concentrate and municipal wastewater; specifically:

[0034] The reverse osmosis concentrate first enters the fine screen to intercept any small suspended solids that may be present in the water, such as particulate matter and fibrous impurities remaining from membrane cleaning, to prevent clogging of downstream equipment. The concentrate passing through the fine screen then enters the security filter (usually a 5-10μm precision filter element) to further filter out tiny particles and protect the pipelines and equipment of the subsequent equalization tank and biological system.

[0035] Urban sewage first enters a coarse screen to remove large floating objects such as plastic bags and branches; then it enters a fine screen to intercept smaller suspended objects such as paper scraps and fine fibers; finally, it enters a vortex grit chamber, where the vortex centrifugal force separates inorganic sand particles (such as silt) from the sewage, preventing sand particles from abrading subsequent equipment and from settling in the biological treatment tank and affecting sludge activity.

[0036] The optimal mixing ratio determination unit 103 is used to monitor the water quality data of the pretreated reverse osmosis concentrate, municipal wastewater, and the mixed liquor in the equalization tank, and to determine the optimal mixing ratio of the reverse osmosis concentrate and municipal wastewater; specifically:

[0037] 103-1. Real-time acquisition of water quality data for reverse osmosis concentrate, municipal wastewater, and mixed liquor from the equalization tank; The water quality data for reverse osmosis concentrate includes conductivity (EC1), total dissolved solids (TDS1), hardness (H1), alkalinity (A1), COD (COD1), and pH (pH1); the water quality data for municipal wastewater includes conductivity (EC2), total dissolved solids (TDS2), hardness (H2), alkalinity (A2), COD (COD2), ammonia nitrogen concentration (N2), total phosphorus concentration (P2), and pH (pH2); the water quality data for the mixed liquor from the equalization tank includes conductivity (EC1 ...). m Total dissolved solids (TDS) m ), Hardness (H) m ), alkalinity (A) m pH (pH) m Langerile Saturation Index (LSI) m The carbon, nitrogen, and phosphorus ratio (C / N / P) and the specific oxygen consumption rate of microorganisms (SOUR) m ).

[0038] 103-2. Set the constraints for the mixing ratio. The constraints include:

[0039] Salinity constraint: mixture To prevent excessive salinity from inhibiting or toxicizing microorganisms;

[0040] Scaling constraint: Mixture To control the Langerilla saturation index within the balance range between slight scaling and slight corrosion, thus preventing severe scaling of pipes and equipment;

[0041] pH constraint: Mixture Ensure that the pH of the mixture is within the optimal range for microbial activity;

[0042] Nutrient constraint: Mixture This ensures that the carbon, nitrogen, and phosphorus ratios in the mixture are close to the optimal requirements for biochemical treatment, thus avoiding nutrient imbalance.

[0043] Microbial activity constraint: mixture Ensuring sufficient microbial activity in the mixture is fundamental to the efficiency of subsequent biochemical treatment.

[0044] 103-3. Constructing a multi-objective optimization function ,in, This indicates the processing cost, including energy consumption, reagents, and sludge disposal costs; This indicates the treatment efficiency, including the removal rate of COD from the mixed liquor, the removal rate of ammonia nitrogen, and the removal rate of total phosphorus. This indicates operational risks, including risks of excessive salinity, scaling, and microbial inhibition. , They represent the weighting factors and .

[0045] 103-4. Construct a BP neural network. The BP neural network includes an input layer, a hidden layer, and an output layer. The input layer has 15 input nodes, and the output layer has 6 output nodes. The 15 input nodes of the input layer correspond to 6 water quality data points of reverse osmosis concentrate, 8 water quality data points of urban sewage, and the mixing ratio, respectively. The 6 output nodes of the output layer correspond to 5 mixing constraint parameters and multi-objective optimization functions of the mixed liquor, respectively.

[0046] Obtain water quality data of reverse osmosis concentrate, urban sewage and equalization tank mixed liquor within a preset historical period, as well as the mixing ratio of reverse osmosis concentrate and urban sewage, treatment cost data, treatment efficiency data and operational risk data. Perform preprocessing on the data, including outlier removal and normalization, and use the preprocessed data to form the training set data.

[0047] The weights and hyperparameters of the BP neural network are randomly initialized. The training set data is input into the input layer of the BP neural network. After nonlinear transformation by the hidden layer, the output layer outputs the prediction result.

[0048] Gradient descent is used to minimize the prediction error. The weights and biases of the input layer, hidden layer and output layer are iteratively updated until the prediction error MSE < 0.001, at which point training stops and a trained BP neural network is obtained.

[0049] 103-5. Introducing a genetic algorithm, the initial mixing ratio parameters are encoded as chromosomes. A multi-objective fitness function is constructed to balance the multi-objective optimization function and constraints. Through initial population generation, fitness calculation, genetic operations, and iterative optimization, the optimal mixing ratio is finally output; specifically:

[0050] The initial mixing ratio parameter is digitally encoded through chromosome structure, and the chromosome consists of the initial mixing ratio r of M batches;

[0051] Each chromosome (r value) is input into the trained BP neural network along with the currently monitored real-time data of reverse osmosis concentrate and urban sewage. The network outputs the predicted constraint parameter values ​​and objective function values.

[0052] The fitness function is designed with the goal of minimizing the multi-objective optimization function F(r) and considering the constraints. If the constraint is violated, a penalty term will be imposed, and the formula is as follows: ,in The penalty coefficient is used to define the constraint deviations, which include salinity constraint deviation, scaling constraint deviation, pH constraint deviation, nutrient constraint deviation, and microbial activity constraint deviation. It should be noted that salinity constraint deviation = max(0, TDS) m -8000), scaling constraint deviation = max(0, LSI) m -0.8) + max(0, -0.5-LSI) m pH constraint deviation = max(0, 6.5 - pH) m ) +max(0, pH m -8.0), Nutrient constraint deviation = Microbial activity constraint deviation = max(0, 0.2 - SOUR) m ).

[0053] Genetic algorithms are introduced to perform genetic operations, including selection, crossover, and mutation. Eighty randomized mixing ratio schemes are generated as the initial population to ensure the population covers the effective range of the mixing ratios and avoids the initial solution set being concentrated in local areas. The fitness value of each mixing ratio scheme is obtained. All mixing ratio schemes are sorted from largest to smallest fitness value, and the top 20% of the schemes are selected as parent individuals to generate the next generation. It should be noted that selecting individuals with higher fitness values ​​makes it more likely that they will pass on their superior genes to the next generation. This means that mixing ratio schemes that result in higher processing efficiency, lower overall processing cost, and lower operational risk are more likely to be preserved and inherited.

[0054] From the selected parent individuals, two individuals are randomly chosen for crossover. For the two selected parent individuals, starting from the random crossover point of the chromosomes, the mixing ratio parameter r of the corresponding batch is exchanged. It should be noted that the purpose of the crossover operation is to create offspring individuals with new gene combinations by exchanging part of the initial mixing ratio parameters of the two parent individuals. This allows for the combination of advantageous genes from different mixing ratio schemes, potentially producing new mixing ratio schemes that better meet the requirements of high efficiency, low cost, and low risk. For the mutation operation, the initial mixing ratio parameters of each individual are mutated. Specifically, for each batch... The mixing ratio r is randomly adjusted each time, with an adjustment range of ±0.02 (i.e., ±2 percentage points), while ensuring that the adjusted r is still within the preset effective range of the mixing ratio, thus avoiding the algorithm getting trapped in local optima. It should be noted that the mutation operation helps the algorithm continuously optimize the mixing ratio strategy under conditions of water quality fluctuations, such as a sudden increase in TDS of RO concentrate and fluctuations in COD of urban sewage, so as to maintain the synergistic treatment effect of the system. In this way, the mixing ratio scheme is continuously iterated until the preset maximum number of iterations (such as 100 times) is reached, and the mixing ratio r corresponding to the individual with the highest fitness is output, which is the optimal mixing ratio.

[0055] It should be noted that the optimal mixing ratio in this application is the target volume fraction of reverse osmosis concentrate in the total volume of the mixed solution. The preset effective range of the mixing ratio is a range pre-set based on engineering requirements such as water quality constraints, microbial activity, and treatment economy of the synergistic treatment system, ensuring that the mixing ratio will not cause the treatment system to fail, but will also give full play to the synergistic advantages of RO concentrate and municipal wastewater.

[0056] The intelligent control unit 104 is used to determine the influent flow rates of reverse osmosis concentrate and municipal sewage into the equalization tank according to the optimal mixing ratio, and to control the influent flow rates of the two through a variable frequency pump accordingly.

[0057] It should be noted that the process for determining the influent flow rates of reverse osmosis concentrate and municipal sewage into the equalization tank is as follows:

[0058] Obtain the preset target total influent flow rate of the equalization tank. According to the formula The influent flow rate of reverse osmosis concentrate entering the equalization tank was calculated. Inflow rate of urban sewage into the equalization tank ;

[0059] It should be noted that the preset total influent target flow rate of the equalization tank is based on the fact that the equalization tank is equipped with a stirrer. The stirrer performs the corresponding stirring operation to ensure that the reverse osmosis concentrate and the urban sewage are fully mixed and uniform. This allows the high salinity, hardness, and alkalinity of the reverse osmosis concentrate to be fully mixed with the pollutants and nutrients in the urban sewage, forming a relatively uniform mixed liquid.

[0060] This invention designs differentiated pretreatment processes based on the characteristics and impurity types of reverse osmosis concentrate and municipal sewage. This effectively removes impurities from both sources that pose significant risks to subsequent treatment, while preventing equipment blockage, wear, and siltation in the biological treatment tank, extending equipment lifespan and reducing maintenance frequency and costs. Through multi-constraint design of the mixing ratio, a safe range is defined for subsequent biological treatment from a water quality perspective. A BP neural network, based on historical data, accurately predicts the parameters and overall benefits of the mixed solution under different mixing ratios. Combining multi-objective optimization functions and genetic algorithms, the objective function integrates treatment cost, treatment efficiency, and operational risk, ensuring that the mixing ratio does not merely reduce costs or increase efficiency. The genetic algorithm, under the premise of satisfying constraints, selects the optimal ratio that balances these three factors, avoiding the fixed ratios of traditional experience and achieving adaptive operation. This ensures that the selected mixing ratio maximizes the overall effect of synergistic pollutant removal, rather than optimizing a single indicator, and better meets the multi-indicator requirements of actual emission standards.

[0061] Biochemical processing module 200, used for employing A 2 The O process involves biologically treating the mixed water after optimal mixing ratio to obtain biologically treated effluent. Simultaneously, the biological treatment information is monitored to determine whether the treatment is up to standard. If the result is unsatisfactory, the cause of the current failure is identified, and corresponding early warning feedback is provided. Specifically:

[0062] Please see Figure 3 As shown, the biochemical treatment module is equipped with a biochemical reaction unit 201, including an anaerobic reaction tank, an anoxic reaction tank, an aerobic reaction tank, and a sedimentation tank.

[0063] It should be noted that, based on A 2 The process steps for biochemical treatment using the O process are as follows:

[0064] A1. Raw wastewater enters the anaerobic reactor to release phosphorus and undergo ammonification: The pretreated mixed water first enters the anaerobic reactor, where it mixes with phosphorus-containing sludge returned from the sedimentation tank. Under anaerobic conditions, polyphosphate-accumulating bacteria release the phosphates they have stored within, while simultaneously converting organic pollutants in the wastewater into their own stored organic matter. Furthermore, organic nitrogen in the wastewater undergoes ammonification here, transforming into ammonia nitrogen.

[0065] A2. Wastewater enters the anoxic reaction tank for denitrification: Wastewater treated in the anaerobic tank flows into the anoxic reaction tank, while the nitrified liquid from the aerobic tank enters here through internal circulation. In the anoxic environment, denitrifying bacteria use the organic carbon source in the wastewater to reduce nitrate nitrogen to nitrogen gas and release it into the atmosphere, thus realizing the denitrification process.

[0066] A3. Wastewater enters the aerobic reaction tank for nitrification, phosphorus absorption, and BOD removal: The effluent from the anoxic tank enters the aerobic reaction tank, where sufficient dissolved oxygen is maintained through aeration. Ammonia nitrogen is oxidized into nitrate nitrogen by nitrifying bacteria (nitrification process); polyphosphate-accumulating bacteria excessively absorb phosphates from the wastewater (phosphorus absorption process); at the same time, the remaining organic pollutants are degraded and removed by aerobic microorganisms.

[0067] A4. Wastewater enters the sedimentation tank for solid-liquid separation: effluent from the aerobic tank flows into the sedimentation tank for sludge-water separation. The treated water is discharged as compliant wastewater; part of the settled sludge is returned to the anaerobic tank via the sludge return system (containing phosphorus sludge) to maintain the microbial population and achieve phosphorus recycling; the other part is discharged as excess sludge, ultimately achieving complete phosphorus removal.

[0068] A5. Sludge and nitrification liquor circulation: Phosphorus-containing sludge from the sedimentation tank is returned to the anaerobic tank to provide phosphorus release and uptake conditions for polyphosphate-accumulating bacteria; nitrification liquor from the aerobic tank is returned to the anoxic tank to provide a substrate for denitrification bacteria and ensure denitrification efficiency.

[0069] The biochemical treatment module 200 is also equipped with a biochemical treatment monitoring unit 202 and a biological treatment analysis feedback unit 203.

[0070] The biochemical treatment monitoring unit 202 is used to monitor current biochemical treatment information, including anaerobic tank information, anoxic tank information, aerobic tank information, and sludge zone information.

[0071] It should be noted that the anaerobic tank information includes the oxidation-reduction potential (ORP) value and volatile organic acid (VFA) concentration at each monitoring time point of the current biochemical treatment anaerobic tank.

[0072] Information on the anoxic tank includes monitoring data for each time point in the current biological treatment anaerobic tank, information on the aerobic tank, and information on the sludge zone.

[0073] The biological treatment analysis feedback unit 203 is used to analyze the current biochemical treatment qualification index based on the current biochemical treatment information, and to make a biochemical treatment qualification judgment. When the judgment result is unqualified, it confirms the cause of the current biochemical treatment failure and provides corresponding early warning feedback.

[0074] The analysis of the current biochemical treatment qualification index includes:

[0075] The reference ranges for ORP and VFA concentration in the anaerobic tank were extracted from the reference information of the biochemical treatment. ORP is a direct quantitative indicator of the reducing power of the anaerobic environment. Its reference range can ensure that polyphosphate-accumulating bacteria become the dominant bacteria in the anaerobic zone, preferentially obtaining energy through phosphorus release and absorbing VFA, laying the foundation for excessive phosphorus absorption in the subsequent aerobic stage, while inhibiting the interference of methanogens and aerobic bacteria. VFA concentration directly determines the sufficiency of phosphorus release by polyphosphate-accumulating bacteria. Polyphosphate-accumulating bacteria need to obtain energy by releasing polyphosphate in their bodies in order to absorb VFA and synthesize PHB (cellular energy storage substance). Its reference range can balance carbon source sufficiency and pH stability, ensuring that the phosphorus release process and carbon source storage are completed simultaneously.

[0076] Obtain the ORP value and VFA concentration at each monitoring time point of the current biochemical treatment, and compare them with the reference reasonable ranges for ORP and VFA concentration, respectively, to screen out the number of monitoring points where the ORP anaerobic environment deviates from the monitoring point. And the number of VFA anaerobic environment deviation monitoring points The values ​​were then compared with the number of monitoring time points, and the ratios were recorded as follows: and ;

[0077] If the deviation monitoring point of a certain ORP anaerobic environment is the same as the deviation monitoring point of a certain VFA anaerobic environment, then this monitoring time point is recorded as the overlapping monitoring time point, and the number of overlapping monitoring time points is counted accordingly. Through formula The phosphorus release qualification index of the anaerobic tank was calculated. ,in, Indicates the number of monitoring time points; It is the proportion of monitoring points with ORP within a reasonable range out of the total number of monitoring points. It is the proportion of monitoring points where VFA is within a reasonable range to the total number of monitoring points; the product of the two reflects the basic compliance level of both ORP and VFA. The higher the compliance rate of the two parameters, the closer the numerator is to 1, and the better the basic conditions for phosphorus release. This represents the percentage of monitoring points where both ORP and VFA deviate from their reasonable ranges (i.e., the proportion of overlapping monitoring points). These points indicate that the anaerobic pond is experiencing both environmental anomalies and insufficient carbon sources, and their inhibitory effect on phosphorus release is far stronger than that of a single parameter deviation. Therefore, the percentage is calculated using the denominator. To amplify its negative impact: the more overlapping anomalies there are, the larger the denominator, and the lower the phosphorus release qualification index of the anaerobic tank.

[0078] Similarly, the reference ranges for nitrate nitrogen concentration and carbon-to-nitrogen ratio at the anoxic tank outlet, the reference range for DO in the aerobic tank, and the reference threshold for ammonia nitrogen removal rate are extracted from the reference information of the biochemical treatment. The denitrification qualification index of the anoxic tank is obtained according to the analysis method of the phosphorus release qualification index of the anaerobic tank. and the nitrification qualification index of the aerobic tank ;

[0079] The reference sludge concentration range, reference sludge settling ratio range, and reference sludge volume index range are extracted from the reference information of the biochemical treatment. The sludge concentration, sludge settling ratio, and sludge volume index at each monitoring time point of the current biochemical treatment are obtained, and compared with the reference sludge concentration range, reference sludge settling ratio range, and reference sludge volume index range, respectively. The deviation ratios of the current biochemical treatment's sludge concentration, sludge settling ratio, and sludge volume index are then selected and denoted as follows: It should be noted that the sludge concentration deviation ratio refers to the ratio of the number of sludge concentrations that are not within the reference sludge concentration range at all monitoring time points of the current biochemical treatment to the total number of sludge concentrations; the sludge settling ratio deviation ratio refers to the ratio of the number of sludge settling ratios that are not within the reference sludge settling ratio range at all monitoring time points of the current biochemical treatment to the total number of sludge concentrations; and the sludge volume index deviation ratio refers to the ratio of the number of sludge volume indices that are not within the reference sludge volume index range at all monitoring time points of the current biochemical treatment to the total number of sludge volume indices.

[0080] Extract all deviation values ​​of sludge concentration that are outside the reference sludge concentration range from all monitoring time points of the current biochemical treatment, and sum these deviation values ​​to generate the sludge concentration deviation value for the current biochemical treatment. It should be noted that if the sludge concentration at a certain monitoring time point is greater than the upper limit of the reference sludge concentration range, the deviation value is the difference between the sludge concentration at that monitoring time point and the upper limit value; if the sludge concentration at a certain monitoring time point is less than the lower limit of the reference sludge concentration range, the deviation value is the difference between the sludge concentration at that monitoring time point and the lower limit value.

[0081] Similarly, the deviation value of the sludge settling ratio in the current biochemical treatment was obtained through analysis. Deviation value of sludge volume index ;

[0082] Through formula The current biochemical treatment sludge health status qualification index was calculated. ,in, These represent the median range of reference sludge concentration, the median range of reference sludge settling ratio, and the median range of reference sludge volume index, respectively.

[0083] Statistics on the current biochemical treatment qualification index , , The qualified index weights for the anaerobic tank, anoxic tank, aerobic tank, and sludge zone are defined respectively in this application. .

[0084] The current biochemical treatment qualification index is compared with the preset biochemical treatment qualification index threshold. If the current biochemical treatment qualification index is less than the preset biochemical treatment qualification index threshold, the current biochemical treatment is deemed unqualified. It should be noted that the biochemical treatment qualification index threshold is a weighted average of the minimum values ​​of the phosphorus release qualification index in the anaerobic tank, the denitrification qualification index in the anoxic tank, the nitrification qualification index in the aerobic tank, and the sludge health status qualification index under standard biochemical treatment conditions. It represents the minimum qualification standard for biochemical treatment.

[0085] The specific process for identifying the causes of current unqualified biochemical treatment is as follows:

[0086] like If the phosphorus release index is less than the set threshold for anaerobic ponds, then the destruction of the anaerobic environment will be considered a cause of non-compliance in biochemical treatment. It should be noted that the threshold for anaerobic pond phosphorus release index refers to the lowest value of the phosphorus release index under standard biochemical treatment conditions. The calculation method for the threshold for anaerobic pond phosphorus release index is the same as that for the anaerobic pond phosphorus release index, and will not be repeated here. In addition, the value of the threshold for anaerobic pond phosphorus release index shall be set by those skilled in the art based on the requirements for the phosphorus release function of the anaerobic pond during the biochemical treatment process.

[0087] like If the denitrification qualification index of the anoxic tank is less than the set threshold, then the low efficiency of anoxic denitrification will be regarded as the cause of the failure of biological treatment.

[0088] like If the aerobic nitrification qualification index is less than the set threshold, then the deviation of aerobic nitrification will be regarded as the cause of the failure of biological treatment.

[0089] like If the sludge health status index is lower than the set threshold, then sludge deactivation will be considered as a cause of non-compliance in biochemical treatment.

[0090] In conducting A-based 2 When monitoring and analyzing the biochemical treatment of the O process, the monitoring information of the anaerobic tank, anoxic tank, aerobic tank and sludge is used to identify the causes of non-compliance in the biochemical treatment, discover potential problems in a timely manner, improve the pertinence of tracing the source of biochemical treatment problems, accurately locate the deep process causes that lead to non-compliance of the final effluent, and provide a clear decision-making direction for operation and control.

[0091] The deep treatment module 300 is used to deeply purify and desalinate biological effluent using a dual-membrane process of ultrafiltration (UF) and reverse osmosis (RO), producing high-quality reclaimed water and realizing the resource utilization of concentrated water; at the same time, it monitors the current UF membrane status data and RO membrane status data, assesses the risk of UF membrane fouling and RO membrane scaling in real time, and adaptively optimizes the backwashing frequency and scale inhibitor dosage.

[0092] Please see Figure 4 As shown, the deep treatment module 300 is equipped with a membrane separation device 301, a concentrate recovery tank 302, and a secondary concentrate storage tank 303. The membrane separation device 301 adopts a dual-membrane process combining ultrafiltration membrane (UF membrane) and reverse osmosis membrane (RO membrane). The ultrafiltration membrane is used to remove suspended solids and colloidal substances from the biochemical effluent, and the reverse osmosis membrane is used for further desalination and impurity removal to produce high-quality reclaimed water. The reverse osmosis membrane uses a fouling-resistant membrane element. The clean water reclaimed water tank 302 is used to store the high-quality fresh water produced by the membrane separation device, which can be directly reused for industrial cooling, greening, road cleaning, etc., realizing the resource utilization of concentrate. The secondary concentrate storage tank 303 is used to collect the secondary concentrate produced by the reverse osmosis unit, which can be returned to the front end of the system for recycling.

[0093] The deep treatment module is also equipped with a UF membrane fouling monitoring unit 304, a UF membrane fouling determination unit 305, a backwashing frequency adjustment unit 306, an RO membrane fouling monitoring unit 307, an RO membrane fouling determination unit 308, and a scale inhibitor adjustment unit 309.

[0094] UF membrane fouling monitoring unit 304 is used to acquire current ultrafiltration membrane status data, establish an ultrafiltration membrane status analysis model, and generate a UF membrane fouling index; specifically:

[0095] The time required for the ultrafiltration membrane to filter a preset volume of water sample at the previous monitoring time point corresponding to each current monitoring time point is denoted as . Simultaneously, the time required for the ultrafiltration membrane to filter a preset volume of water sample at each monitoring time point is obtained, denoted as . Let i represent the number of each monitoring time point, i = 1, 2, ..., n, where n represents the total number of monitoring time points. This can be determined using the formula... The clogging density index of the ultrafiltration membrane at each monitoring time point was calculated. ,in, This indicates the total monitoring time corresponding to two adjacent monitoring time points. The degree of UF membrane clogging is quantified by the change in filtration time for the same volume of water sample. The more clogged the UF membrane, the longer it takes to filter the same volume of water sample. The above formula normalizes the proportion of time change into a clogging density index, which reflects the degree of clogging aggravation within a unit monitoring cycle.

[0096] Obtain the transmembrane pressure difference of the ultrafiltration membrane at each monitoring time point. Membrane flux Silt density index Through formula Calculate the state values ​​of the generated ultrafiltration membrane , These represent the preset reference values ​​for transmembrane pressure difference, membrane flux, and siltation density index, respectively. The normalization function is used to define the following parameters: a preset transmembrane pressure differential benchmark value, corresponding to the pressure differential under clean membrane conditions, serving as a reference baseline for the degree of fouling; a preset membrane flux benchmark value, corresponding to the flux under clean membrane conditions, serving as a reference baseline for permeability; and a preset sludge density index benchmark value, corresponding to the SDI under clean membrane conditions, serving as a reference baseline for the degree of clogging. The normalization function transforms these multi-dimensional parameters into a unified quantitative value for membrane state, achieving a comprehensive characterization of the degree of fouling.

[0097] It should be noted that transmembrane pressure difference refers to the difference between the pressure on the membrane feed side and the pressure on the membrane permeate side, while membrane flux refers to the ratio of permeate flow rate to membrane area.

[0098] Using the monitoring time point as the x-axis and the ultrafiltration membrane state value as the y-axis, a curve of the current ultrafiltration membrane state value change is constructed. An ultrafiltration membrane state value threshold line is set, and the curve of the ultrafiltration membrane state value change is divided into two parts: the part above the ultrafiltration membrane state value threshold line and the part below the ultrafiltration membrane state value threshold line. The areas of these two parts are calculated by calculus to obtain the first area s1 and the second area s2. The first area s1 refers to the area corresponding to the part below the ultrafiltration membrane state value threshold line, and the first area s2 refers to the area corresponding to the part above the ultrafiltration membrane state value threshold line.

[0099] The slope of each monitoring time point is extracted from the ultrafiltration membrane state value change curve. A positive slope indicates that the ultrafiltration membrane state value is increasing and the state is deteriorating, while a negative slope indicates that the ultrafiltration membrane state value is decreasing and the state is improving. All slopes with positive slopes are summed to generate the slope increment value. Accumulate all slopes with negative values ​​to generate the slope reduction value. ;

[0100] According to the formula The ultrafiltration membrane fouling index was calculated. ; The hyperbolic tangent normalization function maps the results of the trend ratio and the state distribution ratio to the 0-1 interval to obtain the final pollution index FI. The larger the FI, the more severe the membrane pollution.

[0101] UF membrane fouling determination unit 305 is used to determine whether the UF membrane is currently fouled based on the UF membrane fouling index; specifically:

[0102] When the ultrafiltration membrane fouling index is less than or equal to the ultrafiltration membrane fouling index threshold, the ultrafiltration membrane is considered to be free of fouling. The smaller the ultrafiltration membrane fouling index, the better the current condition of the ultrafiltration membrane.

[0103] When the ultrafiltration membrane fouling index is greater than the ultrafiltration membrane fouling index threshold, the ultrafiltration membrane is determined to be fouled; the higher the ultrafiltration membrane fouling index, the worse the current condition of the ultrafiltration membrane.

[0104] The backwash frequency adjustment unit 306 is used to perform backwash frequency correction analysis of the ultrafiltration membrane when the current UF membrane is fouled, generate the backwash frequency adjustment value of the ultrafiltration membrane, and adjust the backwash frequency of the ultrafiltration membrane according to the backwash frequency adjustment value.

[0105] Substitute the current ultrafiltration membrane fouling index into the formula. The backwash frequency correction steering weight was calculated. , This indicates the preset ultrafiltration membrane fouling index threshold. This indicates the baseline correction guidance weight corresponding to the deviation of the ultrafiltration membrane fouling index; the preset ultrafiltration membrane fouling index threshold is the critical value for determining whether the membrane needs to adjust the backwashing frequency; the preset baseline correction positive weight corresponds to the basic weight for adjusting the backwashing frequency when FI=FI0. Based on the difference between the membrane fouling index and the preset threshold, the correction weight of the backwashing frequency is dynamically calculated. The more severe the fouling, the greater the correction weight and the higher the backwashing frequency, thus achieving on-demand backwashing.

[0106] The backwash frequency correction guidance weight is matched with the compensation correction calibration frequency corresponding to each preset correction guidance weight to obtain the compensation correction calibration frequency. The sum of the compensation correction calibration frequency and the set backwash frequency is used as the backwash frequency adjustment value.

[0107] It should be noted that by integrating real-time transmembrane pressure difference, membrane flux, and clogging density index into ultrafiltration membrane state values ​​and constructing an ultrafiltration membrane state curve, and then performing time-domain analysis and trend monitoring based on the ultrafiltration membrane state curve to generate an ultrafiltration membrane fouling index, a multi-dimensional assessment from static accumulation and dynamic trends is achieved, making membrane fouling assessment more comprehensive and in line with the dynamic changes in actual operation. When membrane fouling occurs in the ultrafiltration membrane, a backwashing frequency adjustment analysis model is constructed to achieve dynamic adaptive adjustment of the backwashing frequency, avoiding the problems of excessive backwashing leading to resource waste or insufficient backwashing leading to increased membrane fouling caused by a fixed backwashing frequency.

[0108] The RO membrane fouling monitoring unit 307 is used to acquire the current status data of the reverse osmosis membrane, establish a reverse osmosis membrane status analysis model, and generate an RO membrane fouling risk index.

[0109] Obtain the Langerier Saturation Index (LSI) of the reverse osmosis membrane at each current monitoring time point;

[0110] It should be noted that the Langerier saturation index is obtained as follows: the actual pH value on the concentrate side of the reverse osmosis membrane is collected and recorded as pH, and then calculated using the formula... The Langrillly saturation index LSI was calculated, where, The saturation pH value is the pH value at which calcium carbonate just reaches saturation under specific calcium hardness and alkalinity conditions in the concentrate side water. When LSI > 0, it indicates a tendency to scale, and the higher the LSI value, the higher the risk index.

[0111] Using each monitoring time point as the horizontal axis and the Langerile saturation index (LSI) as the vertical axis, a curve of the Langerile saturation index of the reverse osmosis membrane at the current time is constructed. From this curve, the number of peak points, the number of valley points, the difference in Langerile saturation index between each peak point and its adjacent valley point, and the interval time are located.

[0112] The maximum value is selected from the number of peak points and the number of valley points, and denoted as . ,in The number of peak points. This represents the number of valley points;

[0113] The maximum value is selected from the differences in Langerile saturation exponents between each peak point and its adjacent valley point, denoted as . Simultaneously, the average peak-to-valley Langerier saturation index difference is generated by calculating the mean, denoted as... ;

[0114] The minimum value is selected from the intervals between the peak point and its adjacent valley point, and denoted as . Simultaneously, the average peak-to-valley interval duration is calculated using the mean, denoted as... ;

[0115] Through formula

[0116] The fluctuation value of the reverse osmosis membrane under the current Langerier saturation index was calculated. ,in, These represent the preset number of reference peak and valley points, the first peak-valley difference, the second peak-valley difference, the first peak-valley interval duration, and the second peak-valley interval duration, respectively. Indicates the first volatility coefficient and the second volatility coefficient;

[0117] The concentrations of various sparingly soluble salt ions and water temperature on the feed side of the reverse osmosis membrane at each monitoring time point are obtained using an online ion analyzer and temperature sensor. The sparingly soluble salts include, but are not limited to, CaSO4, BaSO4, and SrSO4. A pre-set water temperature-sparing salt solubility product constant Ksp correspondence table is used to match the real-time water temperature with the pre-set water temperature-sparing salt solubility product constant Ksp correspondence table to obtain the solubility product constant Ksp of various sparingly soluble salts corresponding to the water temperature at each previous monitoring time point.

[0118] The influent flow rate on the feed side and the effluent flow rate on the effluent side of the reverse osmosis membrane are obtained by a flow meter at each monitoring time point. The ratio of the effluent flow rate to the influent flow rate is calculated to generate the recovery rate Y. The recovery rate Y is then calculated using the formula... The concentration factor CF is calculated; the concentration factor reflects the factor by which ions on the concentrate side are concentrated due to water recovery.

[0119] The concentrations and concentration factors of various sparingly soluble salt ions on the feed side of the reverse osmosis membrane at each current monitoring time point are multiplied to obtain the concentration of sparingly soluble salt ions on the concentrate side of the reverse osmosis membrane at each current monitoring time point. This yields the product of sparingly soluble salt ions on the concentrate side of the reverse osmosis membrane at each current monitoring time point. It should be noted that the product of sparingly soluble salt ions on the concentrate side is the product of the concentrations of sparingly soluble salt cations and anions.

[0120] The ratio of the sparingly soluble salt ion product to the solubility product constant on the concentrate side is calculated to generate the sparingly soluble salt saturation of the reverse osmosis membrane at each monitoring time point. When the saturation is greater than 1, it indicates that the sparingly soluble salt on the concentrate side is oversaturated, and there is a risk of scaling. The higher the sparingly soluble salt saturation value, the higher the risk index.

[0121] Similarly, the current saturation fluctuation value of the reverse osmosis membrane can be obtained by the same method as obtaining the current Langerier saturation index fluctuation value of the reverse osmosis membrane.

[0122] The scaling risk index of the reverse osmosis membrane at the current time is generated by summing the current Langerier saturation index fluctuation value and the saturation fluctuation value.

[0123] The RO membrane fouling determination unit 308 is used to determine whether there is a fouling risk in the current RO membrane based on the RO membrane fouling risk index.

[0124] When the RO membrane fouling risk index is less than or equal to the RO membrane fouling risk index threshold, it is determined that there is no fouling risk in the current RO membrane; the smaller the RO membrane fouling risk index, the better the current RO membrane condition.

[0125] When the RO membrane fouling risk index is greater than the RO membrane fouling risk index threshold, it is determined that the current RO membrane has a fouling risk; the higher the RO membrane fouling risk index, the worse the current RO membrane condition.

[0126] The scale inhibitor adjustment unit 309 is used to perform scale inhibitor correction analysis on the RO membrane if there is a risk of scaling on the current RO membrane, generate an adjustment value for the scale inhibitor dosage of the RO membrane, and adjust the scale inhibitor dosage of the RO membrane according to the adjustment value.

[0127] Substitute the current RO membrane fouling risk index into the formula. The corrective guiding weight for scale inhibitor dosage was calculated. , This indicates the preset threshold for the RO membrane fouling risk index. This indicates the benchmark correction guidance weight corresponding to the deviation of the preset RO membrane fouling risk index;

[0128] The scale inhibitor dosage correction guide weight is matched with the compensation correction calibration dosage corresponding to each preset correction guide weight to obtain the compensation correction calibration dosage. The sum of the compensation correction calibration dosage and the set scale inhibitor dosage is used as the scale inhibitor dosage adjustment value.

[0129] It should be noted that by constructing variation curves for the Langerile saturation index and the saturation of sparingly soluble salts, and then analyzing the fluctuations of the Langerile saturation index and the saturation of sparingly soluble salts based on the peak and valley characteristics of the curves, a comprehensive assessment of various scaling risks, such as calcium carbonate scale and sulfate scale, can be achieved. This breaks through the limitations of traditional methods that rely solely on calcium carbonate scaling tendency (LSI) for judgment, avoiding misjudgments or omissions caused by one-sided monitoring, and significantly improving the completeness and reliability of risk assessment. When there is a risk of scaling in the reverse osmosis membrane, an analysis model for adjusting the scale inhibitor dosage is constructed to achieve dynamic adaptive optimization of scale inhibitor dosage. This avoids the problems of insufficient scale inhibition due to a fixed dosage leading to aggravated membrane scaling, or excessive dosage leading to resource waste and increased operating costs.

[0130] Please see Figure 5 As shown, a method for the synergistic treatment of reverse osmosis concentrate and municipal wastewater includes the following steps:

[0131] S1. Pretreatment: Collect reverse osmosis concentrate and municipal wastewater and pretreat them. Monitor the water quality data of the pretreated reverse osmosis concentrate and municipal wastewater to determine the optimal mixing ratio of reverse osmosis concentrate and municipal wastewater. Based on the optimal mixing ratio, perform corresponding control operations.

[0132] S2, Biochemical treatment: Using A 2 The O process performs biological treatment on the mixed water after it has been adjusted to the optimal mixing ratio to obtain biological effluent; at the same time, it monitors the biological treatment information to determine whether the biological treatment is qualified. When the judgment result is unqualified, it identifies the cause of the current unqualified biological treatment and provides corresponding early warning feedback.

[0133] S3. Deep Treatment: The system employs a dual-membrane process of ultrafiltration (UF) and reverse osmosis (RO) to deeply purify and desalinate the biochemical effluent, producing high-quality reclaimed water and realizing the resource utilization of concentrated water. Simultaneously, it monitors the current UF membrane status data and RO membrane status data, assesses the risk of UF membrane fouling and RO membrane scaling in real time, and adaptively optimizes the backwashing frequency and scale inhibitor dosage.

[0134] It should be noted that the process flow diagram for the co-treatment of reverse osmosis concentrate and municipal wastewater in this invention can be found in the attached diagram. Figure 6 As shown.

[0135] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0136] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A system for the synergistic treatment of reverse osmosis concentrate and municipal wastewater, characterized in that, include: The pretreatment module is used to collect reverse osmosis concentrate and municipal sewage and pretreat them, monitor the water quality data of the pretreated reverse osmosis concentrate and municipal sewage, determine the optimal mixing ratio between the two, and perform corresponding control operations based on the optimal mixing ratio. Biochemical processing module, used to employ A 2 The O process performs biological treatment on the mixed water after the optimal mixing ratio is adjusted to obtain biological effluent; at the same time, the biological treatment information is monitored to determine whether the biological treatment is qualified. When the judgment result is unqualified, the cause of the current unqualified biological treatment is identified and corresponding early warning feedback is given. The deep treatment module is used to deeply purify and desalinate biological effluent using a dual-membrane process of ultrafiltration and reverse osmosis, producing high-quality reclaimed water and realizing the resource utilization of concentrated water; at the same time, it monitors the current UF membrane status data and RO membrane status data, assesses the risk of UF membrane fouling and RO membrane scaling in real time, and adaptively optimizes the backwashing frequency and scale inhibitor dosage.

2. The system for the synergistic treatment of reverse osmosis concentrate and municipal wastewater according to claim 1, characterized in that, The preprocessing module includes: The concentrate and wastewater collection unit is used to simultaneously collect concentrate from the reverse osmosis system and municipal wastewater; The pretreatment unit is used to pretreat the reverse osmosis concentrate and municipal sewage separately to obtain pretreated reverse osmosis concentrate and municipal sewage. The optimal mixing ratio determination unit is used to monitor the water quality data of the pretreated reverse osmosis concentrate, municipal wastewater, and the mixed liquor in the equalization tank, and to determine the optimal mixing ratio of reverse osmosis concentrate and municipal wastewater. The intelligent control unit is used to determine the influent flow rates of reverse osmosis concentrate and municipal sewage into the equalization tank according to the optimal mixing ratio, and the influent flow rates of the two are controlled accordingly by a variable frequency pump.

3. The system for the synergistic treatment of reverse osmosis concentrate and municipal wastewater according to claim 2, characterized in that, The specific process for determining the optimal mixing ratio of reverse osmosis concentrate and municipal wastewater is as follows: 103-1. Real-time acquisition of water quality data for reverse osmosis concentrate, urban sewage, and mixed liquor from the equalization tank; 103-2. Set constraints on the mixing ratio, including salinity constraints, scaling constraints, pH constraints, nutrient constraints, and microbial activity constraints; 103-3. Constructing a multi-objective optimization function ,in, Indicates processing cost, Indicates processing efficiency. Indicates operational risk. These represent the weighting factors; 103-4. Construct a BP neural network. The BP neural network includes an input layer, a hidden layer, and an output layer. The input layer has 15 input nodes, and the output layer has 6 output nodes. The 15 input nodes of the input layer correspond to 6 water quality data points of reverse osmosis concentrate, 8 water quality data points of urban sewage, and the mixing ratio, respectively. The 6 output nodes of the output layer correspond to 5 mixing constraint parameters and multi-objective optimization functions of the mixed liquor, respectively. Obtain water quality data of reverse osmosis concentrate, urban sewage and equalization tank mixed liquor within a preset historical period, as well as the mixing ratio of reverse osmosis concentrate and urban sewage, treatment cost data, treatment efficiency data and operational risk data. Preprocess the data and use the preprocessed data to form the training set data. The weights and hyperparameters of the BP neural network are randomly initialized. The training set data is input into the input layer of the BP neural network. After nonlinear transformation by the hidden layer, the output layer outputs the prediction result. Gradient descent is used to minimize the prediction error. The weights and biases of the input layer, hidden layer and output layer are iteratively updated until the prediction error MSE < 0.001, at which point training stops and a well-trained BP neural network is obtained. 103-5. Introduce a genetic algorithm to encode the initial mixing ratio parameters into chromosomes, construct a multi-objective fitness function to balance the multi-objective optimization function and constraints, and finally output the optimal mixing ratio through initial population generation, fitness calculation, genetic operations and iterative optimization.

4. The system for the synergistic treatment of reverse osmosis concentrate and municipal wastewater according to claim 1, characterized in that, The biochemical processing module includes: The biochemical reaction unit includes an anaerobic reaction tank, an anoxic reaction tank, an aerobic reaction tank, and a sedimentation tank; The biochemical treatment monitoring unit is used to monitor current biochemical treatment information, including anaerobic tank information, anoxic tank information, aerobic tank information, and sludge zone information; The biological treatment analysis feedback unit is used to analyze the current biochemical treatment qualification index based on the current biochemical treatment information, and to make a biochemical treatment qualification judgment. When the judgment result is unqualified, it confirms the cause of the current biochemical treatment failure and provides corresponding early warning feedback.

5. The system for the synergistic treatment of reverse osmosis concentrate and municipal wastewater according to claim 4, characterized in that, The analysis of the current biochemical treatment qualification index includes: Extract the reference reasonable range of ORP and VFA concentration in the anaerobic tank, obtain the ORP value and VFA concentration at each monitoring time point, compare and screen the number of ORP and VFA deviations from the monitoring point, and compare them with the number of monitoring time points to generate the corresponding ratio results. If the deviation monitoring point of a certain ORP anaerobic environment is the same as that of a certain VFA anaerobic environment, then this monitoring time point is recorded as a coincident monitoring time point, and the number of coincident monitoring time points is counted; based on this formula, the phosphorus release qualification index of the anaerobic tank is obtained through analysis. ; Similarly, the nitrate nitrogen concentration and carbon-to-nitrogen ratio at the outlet of the anoxic tank were extracted from the reasonable range, and the dissolved oxygen (DO) concentration and ammonia nitrogen removal rate were extracted from the reasonable range and threshold, respectively. The denitrification qualification index of the anoxic tank was obtained using the same analytical method as the phosphorus release qualification index of the anaerobic tank. and the nitrification qualification index of the aerobic tank ; The reference sludge concentration range, reference sludge settling ratio range, and reference sludge volume index range are extracted. The sludge concentration, sludge settling ratio, and sludge volume index at each monitoring time point are obtained, and these are compared and screened to identify the current sludge concentration deviation ratio, sludge settling ratio deviation ratio, and sludge volume index deviation ratio. Deviation values ​​where the sludge concentration is outside the reference sludge concentration range at all current monitoring time points are extracted and accumulated to generate the current sludge concentration deviation value. Similarly, the current sludge settling ratio deviation value and sludge volume index deviation value are analyzed. Based on these, the current sludge health status qualification index is calculated using a formulaic analysis. ; The current biochemical treatment pass index is calculated and compared with the preset biochemical treatment pass index threshold. If the current biochemical treatment pass index is less than the preset biochemical treatment pass index threshold, the current biochemical treatment is determined to be unqualified.

6. The system for the synergistic treatment of reverse osmosis concentrate and municipal wastewater according to claim 4, characterized in that, The specific process for identifying the causes of current unqualified biochemical treatment is as follows: like If the phosphorus release index is less than the set threshold for anaerobic ponds, then the destruction of the anaerobic environment will be considered a cause of non-compliance in biochemical treatment; if If the denitrification efficiency is less than the set threshold for the anoxic tank denitrification qualification index, then the low anoxic denitrification efficiency will be considered a cause of failure in biological treatment; if If the nitrification index is less than the set threshold for aerobic nitrification, then the deviation in aerobic nitrification will be considered a cause of non-compliance in biological treatment; if If the sludge health status index is lower than the set threshold, then sludge deactivation will be considered as a cause of non-compliance in biochemical treatment.

7. The system for the synergistic treatment of reverse osmosis concentrate and municipal wastewater according to claim 1, characterized in that, The depth processing module includes: Membrane separation unit, concentrate recovery tank and secondary concentrate storage tank; The UF membrane fouling monitoring unit is used to acquire current ultrafiltration membrane status data, establish an ultrafiltration membrane status analysis model, and generate a UF membrane fouling index. The UF membrane fouling determination unit is used to determine whether the UF membrane is currently fouled based on the UF membrane fouling index. The backwash frequency adjustment unit is used to perform backwash frequency correction analysis when the current UF membrane is fouled, generate a backwash frequency adjustment value for the ultrafiltration membrane, and adjust the backwash frequency of the ultrafiltration membrane according to the backwash frequency adjustment value. The RO membrane fouling monitoring unit is used to acquire current reverse osmosis membrane status data, establish a reverse osmosis membrane status analysis model, and generate an RO membrane fouling risk index. The RO membrane fouling determination unit is used to determine whether there is a risk of fouling in the current RO membrane based on the RO membrane fouling risk index. The scale inhibitor adjustment unit is used to perform scale inhibitor correction analysis on the RO membrane when there is a risk of scaling on the current RO membrane, generate an adjustment value for the scale inhibitor dosage of the RO membrane, and adjust the scale inhibitor dosage of the RO membrane according to the adjustment value.

8. The system for the synergistic treatment of reverse osmosis concentrate and municipal wastewater according to claim 7, characterized in that, The specific process for generating the UF membrane fouling index is as follows: The transmembrane pressure difference, membrane flux and clogging density index of the ultrafiltration membrane are obtained at each monitoring time point to generate the ultrafiltration membrane state value; the current ultrafiltration membrane state value change curve is constructed, and the first area and second area, slope increase value and slope decrease value are extracted from it to generate the ultrafiltration membrane fouling index. The determination of whether the UF membrane is currently contaminated specifically involves: When the ultrafiltration membrane fouling index is less than or equal to the ultrafiltration membrane fouling index threshold, the ultrafiltration membrane is determined to be free of fouling; when the ultrafiltration membrane fouling index is greater than the ultrafiltration membrane fouling index threshold, the ultrafiltration membrane is determined to be fouled. The backwashing frequency adjustment value for generating the ultrafiltration membrane is specifically as follows: The backwash frequency correction guidance weight is obtained by formulating the current ultrafiltration membrane fouling index; the backwash frequency correction guidance weight is matched with the compensation correction calibration frequency corresponding to each preset correction guidance weight to obtain the compensation correction calibration frequency; the sum of the compensation correction calibration frequency and the set backwash frequency is used as the backwash frequency adjustment value.

9. The system for the synergistic treatment of reverse osmosis concentrate and municipal wastewater according to claim 7, characterized in that, The specific process for generating the RO membrane fouling risk index is as follows: Obtain the Langeryl saturation index (LSI) of the reverse osmosis membrane at each monitoring time point, construct the LSI variation curve of the current reverse osmosis membrane, and locate the number of peak points, the number of valley points, the difference in LSI between each peak point and its adjacent valley point, and the interval time; based on this, analyze the fluctuation value of the LSI of the reverse osmosis membrane at the current time. Obtain the concentrations of various sparingly soluble salt ions and water temperature on the feed side of the reverse osmosis membrane at each current monitoring time point; pre-set the water temperature-sparing salt solubility product constant Ksp correspondence table, and match it to obtain the solubility product constant Ksp of various sparingly soluble salts corresponding to the water temperature at each current monitoring time point; Obtain the influent flow rate and effluent flow rate of the reverse osmosis membrane at each monitoring time point, calculate the ratio of the effluent flow rate to the influent flow rate, and generate the recovery rate Y; then use the formula... The concentration factor CF was calculated. The concentrations of various sparingly soluble salt ions and their concentration factors on the feed side of the reverse osmosis membrane at each current monitoring time point are multiplied to obtain the concentrations of sparingly soluble salt ions on the concentrate side of the reverse osmosis membrane at each current monitoring time point, and then the product of sparingly soluble salt ions on the concentrate side of the reverse osmosis membrane at each current monitoring time point is obtained. The ratio of the sparingly soluble salt ion product to the solubility product constant on the concentrate side is calculated to generate the sparingly soluble salt saturation of the reverse osmosis membrane at each monitoring time point. Similarly, the current saturation fluctuation value of the reverse osmosis membrane can be obtained by the same method as obtaining the current Langerier saturation index fluctuation value of the reverse osmosis membrane. The scaling risk index of the reverse osmosis membrane at the current Langerier saturation index and saturation fluctuation value are calculated by summing the current Langerier saturation index and saturation fluctuation value. The determination of whether the current RO membrane has a risk of scaling is specifically as follows: When the RO membrane fouling risk index is less than or equal to the RO membrane fouling risk index threshold, it is determined that the current RO membrane has no fouling risk; when the RO membrane fouling risk index is greater than the RO membrane fouling risk index threshold, it is determined that the current RO membrane has a fouling risk. The adjustment value for the scale inhibitor dosage in generating the RO membrane is specifically as follows: The current RO membrane fouling risk index is used to formulate and analyze the scale inhibitor dosage correction guide weight. The scale inhibitor dosage correction guide weight is matched with the compensation correction calibration dosage corresponding to each preset correction guide weight to obtain the compensation correction calibration dosage. The sum of the compensation correction calibration dosage and the set scale inhibitor dosage is used as the scale inhibitor dosage adjustment value.

10. A method for the co-treatment of reverse osmosis concentrate and municipal wastewater, based on the operation of a co-treatment system for reverse osmosis concentrate and municipal wastewater as described in claims 1-9, characterized in that, Includes the following steps: S1. Pretreatment: Collect reverse osmosis concentrate and municipal sewage and pretreat them. Monitor the water quality data of the pretreated reverse osmosis concentrate and municipal sewage to determine the optimal mixing ratio. Based on the optimal mixing ratio, perform corresponding control operations. S2, Biochemical treatment: Using A 2 The O process performs biological treatment on the mixed water after the optimal mixing ratio is adjusted to obtain biological effluent; at the same time, the biological treatment information is monitored to determine whether the biological treatment is qualified. When the judgment result is unqualified, the cause of the current unqualified biological treatment is identified and corresponding early warning feedback is given. S3. Advanced Treatment: The ultrafiltration and reverse osmosis dual-membrane process is used to deeply purify and desalinate the biological effluent, producing high-quality reclaimed water and realizing the resource utilization of concentrated water; at the same time, the current UF membrane status data and RO membrane status data are monitored to assess the UF membrane fouling risk and RO membrane scaling risk in real time, and adaptively optimize the backwashing frequency and scale inhibitor dosage.

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