A sewage treatment plant aeration quantity dynamic regulation and control method based on digital twinning

Abstract

This invention relates to the field of wastewater treatment technology, and particularly to a method for dynamic control of aeration volume in wastewater treatment plants based on digital twins. The method includes: constructing a digital twin model; inputting collected operating parameters into the model to obtain a predicted dissolved oxygen concentration; acquiring the target and measured dissolved oxygen concentration values ​​and calculating the dissolved oxygen deviation parameter; calculating activity parameters, mass transfer parameters, and energy efficiency parameters based on the operating parameters; determining a comprehensive judgment index based on the above four parameters; determining that the aeration control quality is unqualified when the comprehensive judgment index exceeds a preset threshold, and determining the cause of the unqualification based on the deviation of each parameter; determining the adjustment method based on the cause of the unqualification, determining the adjustment amount based on the predicted value, and feeding back the adjusted operating parameters to the model to update the model parameters. This invention achieves accurate predictive control of aeration volume through digital twin models and multi-parameter collaborative optimization, thereby reducing energy consumption in wastewater treatment.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a method for dynamic control of aeration volume in wastewater treatment plants based on digital twins. Background Technology

[0002] Aerobic biological treatment processes in wastewater treatment plants are crucial for removing organic pollutants and nitrogen. The aeration system provides sufficient dissolved oxygen to microorganisms by injecting air into the aeration tank. Traditional aeration control methods primarily employ PID control, which has the following limitations: First, it is a lag control, relying on real-time feedback, and its response is untimely when influent water quality or quantity changes. Second, it has a single control objective, focusing solely on dissolved oxygen concentration, failing to comprehensively consider factors such as microbial activity and energy consumption. Third, it lacks predictive capabilities, unable to predict water quality trends and only passively responding to disturbances. Fourth, it ignores parameter coupling; dissolved oxygen has complex coupling relationships with aeration rate, microbial biomass, and influent load, which are difficult to handle with traditional control methods. Fifth, it has low energy efficiency, with fixed aeration modes or simple threshold control resulting in energy waste. Digital twin technology, by constructing a digital mapping of the physical system, can achieve real-time monitoring, simulation prediction, and optimized control; however, existing technologies have not yet been effectively applied to the dynamic regulation of aeration rates.

[0003] Chinese Patent Application No. CN202311226102.0 discloses an intelligent control method and system for wastewater treatment. Specifically, after initially deploying dissolved oxygen sensors in the aeration tank of the wastewater treatment plant, the system acquires measured dissolved oxygen values ​​through each sensor and uploads all measured values ​​to a server to obtain measurement data. Then, the measured data is used to analyze dissolved oxygen demand, obtain aeration oxygen anomalies, and finally, intelligently control the aeration equipment in a timely manner based on the aeration oxygen anomalies. This method efficiently and accurately marks the time points of aeration oxygen content collapse, improves the feedback sensitivity to slowly changing polluted water quality or quantity, and predicts the location of dissolved oxygen defects, greatly enhancing the system's fault tolerance and adaptability to complex environments. In addition to saving energy and resources, it also enhances the stability of the aeration system, and the rational allocation of aeration volume ensures the practical efficiency of wastewater treatment in the biological treatment stage.

[0004] However, existing technologies still have the following problems:

[0005] Without constructing a digital twin model to dynamically predict dissolved oxygen concentration and lacking a comprehensive judgment mechanism that coordinates multiple parameters, it is difficult to accurately optimize aeration energy consumption while ensuring effluent quality. Summary of the Invention

[0006] To address this, the present invention provides a dynamic control method for aeration volume in wastewater treatment plants based on digital twins, which overcomes the problems in existing technologies such as the lack of a digital twin model to dynamically predict dissolved oxygen concentration, the lack of a comprehensive judgment mechanism for multi-parameter coordination, and the difficulty in accurately optimizing aeration energy consumption while ensuring effluent quality.

[0007] To achieve the above objectives, this invention provides a method for dynamic control of aeration volume in wastewater treatment plants based on digital twins. It includes:

[0008] Step S1: Construct a digital twin model of the wastewater treatment aeration process, collect the operating parameters of the wastewater treatment aeration process, input the operating parameters into the digital twin model, and obtain the predicted value of dissolved oxygen concentration.

[0009] Step S2: Obtain the preset target value of dissolved oxygen concentration and the measured value of dissolved oxygen concentration collected in real time, and calculate the dissolved oxygen deviation parameter based on the target value and the measured value;

[0010] Step S3: Based on the operating parameters, calculate the activity parameters characterizing the metabolic state of microorganisms, the mass transfer parameters characterizing the gas-liquid oxygen transfer efficiency, and the energy efficiency parameters characterizing the treatment effect per unit of energy consumption.

[0011] Step S4: Based on the dissolved oxygen deviation parameter, the activity parameter, the mass transfer parameter, and the energy efficiency parameter, determine the comprehensive judgment index characterizing the quality of aeration control;

[0012] Step S5: In response to the comprehensive judgment index being greater than the preset comprehensive judgment index, determine that the current aeration control quality is unqualified, and determine the reason for the unqualified aeration control quality based on the deviation of each parameter constituting the comprehensive judgment index.

[0013] Step S6: Determine the adjustment method of the operating parameters based on the reasons for the unqualified aeration control quality, and determine the adjustment amount in combination with the predicted value. Feed back the adjusted operating parameters to the digital twin model to update the digital twin model parameters.

[0014] Further, in step S1, the predicted value of dissolved oxygen concentration is obtained, including:

[0015] A mechanistic model based on the mathematical model of activated sludge was established to describe the organic matter degradation, nitrification and denitrification reactions in the microbial metabolic process, and the first dissolved oxygen prediction value was output.

[0016] A data-driven model based on long short-term memory network is established, using historical dissolved oxygen concentration sequence, historical influent chemical oxygen demand sequence, historical influent flow rate sequence, historical aeration rate sequence and historical water temperature sequence as input features, and outputting a second dissolved oxygen prediction value.

[0017] The first and second predicted dissolved oxygen values ​​are fused together to output the predicted dissolved oxygen concentration.

[0018] Further, in step S2, the dissolved oxygen deviation parameter is calculated, including:

[0019] The measured dissolved oxygen concentration in real time is compared with the preset target dissolved oxygen concentration. The absolute value of the difference is then taken to obtain the dissolved oxygen deviation parameter.

[0020] Further, in step S3, activity parameters characterizing the metabolic state of microorganisms are calculated, including:

[0021] The concentration of volatile suspended solids and the dissolved oxygen consumption rate of the mixture are obtained from the operating parameters.

[0022] The specific oxygen consumption rate is calculated based on the concentration of volatile suspended solids in the mixture and the dissolved oxygen consumption rate.

[0023] A preset reference specific oxygen consumption rate is obtained, and the ratio of the specific oxygen consumption rate to the reference specific oxygen consumption rate is used as a microbial activity parameter.

[0024] Further, in step S3, the mass transfer parameters characterizing the gas-liquid oxygen transfer efficiency are calculated, including:

[0025] Obtain the aeration rate and current dissolved oxygen concentration from the operating parameters;

[0026] Obtain the preset saturated dissolved oxygen concentration and aeration tank volume, calculate the gas-liquid mass transfer coefficient based on the aeration rate, the saturated dissolved oxygen concentration, the current dissolved oxygen concentration and the aeration tank volume, and use the gas-liquid mass transfer coefficient as the mass transfer parameter.

[0027] Further, in step S3, energy efficiency parameters characterizing the treatment effect per unit of energy consumption are calculated, including:

[0028] The influent chemical oxygen demand (COD), effluent COD, and aeration energy consumption are obtained from the aforementioned operating parameters.

[0029] The amount of chemical oxygen demand removed is calculated based on the difference between the influent chemical oxygen demand and the effluent chemical oxygen demand, and the ratio of the amount of chemical oxygen demand removed to the aeration energy consumption is used as an energy efficiency parameter.

[0030] Further, in step S4, a comprehensive judgment index characterizing the quality of aeration control is determined, including:

[0031] Obtain preset dissolved oxygen deviation threshold, activity parameter reference value, mass transfer parameter reference value and energy efficiency parameter reference value;

[0032] The ratio of the dissolved oxygen deviation parameter to the dissolved oxygen deviation threshold is calculated and used as the first deviation value.

[0033] Calculate the ratio of the value after subtracting the activity parameter from the reference value of the activity parameter, and use it as the second deviation value;

[0034] The ratio of the reference value of the mass transfer parameter to the mass transfer parameter is calculated and used as the third deviation value;

[0035] The ratio of the energy efficiency parameter reference value to the energy efficiency parameter is calculated and used as the fourth deviation value;

[0036] The first deviation value, the second deviation value, the third deviation value, and the fourth deviation value are squared, summed, and then the square root is taken to obtain the comprehensive judgment index.

[0037] Further, in step S5, the reasons for the unqualified aeration control quality are determined, including:

[0038] The normalized squared value of the dissolved oxygen deviation parameter is calculated as the first contribution.

[0039] The sum of squares of the deviations of the activity parameter, mass transfer parameter, and energy efficiency parameter is calculated as the second contribution.

[0040] Compare the magnitudes of the first contribution and the second contribution;

[0041] If the first contribution is greater than the second contribution, the reason for non-compliance is determined to be the aeration volume deviation category;

[0042] If the first contribution is not greater than the second contribution, the reason for non-compliance is determined to be the active mass transfer category.

[0043] Further, in step S6, the adjustment method of the operating parameters is determined based on the reason for the unqualified aeration control quality, and the adjustment amount is determined in conjunction with the predicted value, including:

[0044] When the reason for non-compliance is the aeration volume deviation category, the blower frequency adjustment amount is calculated based on the dissolved oxygen deviation parameter and the predicted value, and the blower operating frequency is adjusted according to the blower frequency adjustment amount.

[0045] When the reason for non-compliance is the activity mass transfer category, the aeration disc opening adjustment amount is calculated based on the activity parameters and the mass transfer parameters, and the aeration disc opening is adjusted according to the aeration disc opening adjustment amount.

[0046] Further, in step S6, the digital twin model parameters are updated, including:

[0047] The measured value of the adjusted dissolved oxygen concentration is used as a feedback signal, and the prediction error between the feedback signal and the predicted value of the dissolved oxygen concentration before adjustment is calculated.

[0048] The fusion weights of the mechanistic model and the data-driven model in the digital twin model are dynamically adjusted based on the prediction error to complete the parameter update of the digital twin model.

[0049] Compared with the prior art, the beneficial effects of the present invention are that by constructing a digital twin system that integrates the mechanistic model and the data-driven model, and by dynamically adjusting the prediction weights using an adaptive weighted fusion method, the present invention achieves accurate prediction of dissolved oxygen concentration, overcomes the problem of insufficient prediction accuracy of a single model, and provides a reliable prediction basis for the feedforward control of subsequent aeration volume.

[0050] Furthermore, this invention quantifies microbial metabolic activity by calculating the ratio of specific oxygen consumption rate to reference value, quantifies oxygen transfer efficiency by calculating gas-liquid mass transfer coefficient, and quantifies unit energy consumption treatment effect by calculating the ratio of chemical oxygen demand removal to aeration energy consumption. This enables comprehensive perception and quantitative evaluation of the aeration system's operating status, providing a data foundation for multi-parameter coordinated control.

[0051] Furthermore, this invention constructs a comprehensive quantitative evaluation model for aeration control quality by normalizing and weighting the deviation values ​​of four dimensions: dissolved oxygen deviation, microbial activity, gas-liquid mass transfer efficiency, and energy consumption efficiency. This model enables a comprehensive evaluation of the single-index operation status of the aeration system and provides a unified judgment benchmark for non-compliance diagnosis and control decisions.

[0052] Furthermore, this invention constructs a two-factor diagnostic model for the causes of non-compliance by comparing the contribution of dissolved oxygen deviation with the combined contribution of activity, mass transfer, and energy efficiency. When the contribution of dissolved oxygen deviation is greater than the combined contribution of the three factors, it indicates that the mismatch between aeration supply and demand is the dominant factor leading to non-compliance in quality control, thus classifying it as an aeration deviation category. Conversely, it indicates that insufficient microbial metabolic activity, low gas-liquid oxygen transfer efficiency, or poor energy consumption treatment effect are the main causes, classifying it as an activity-mass transfer category. This diagnostic method simplifies the complex multi-parameter coupling problem into a single-factor comparison, achieving rapid and accurate classification of the causes of non-compliance.

[0053] Furthermore, this invention distinguishes between two causes of non-compliance and adopts differentiated adjustment strategies. In the adjustment of aeration volume deviation, it introduces predicted values ​​for feedforward control to overcome the lag problem of traditional feedback control. In the adjustment of activity and mass transfer, it introduces activity parameters and mass transfer parameters for synergistic optimization and adopts a dynamic allocation strategy for multiple aeration zones, thereby achieving precise, efficient, and dynamic control of aeration volume. Attached Figure Description

[0054] Figure 1 This is a flowchart of the dynamic control method for aeration volume in a wastewater treatment plant based on digital twins, as described in this invention.

[0055] Figure 2 This is a flowchart illustrating the process of determining whether the current aeration control quality is up to standard in the dynamic control method for aeration volume in a wastewater treatment plant based on digital twins, as described in this invention. Detailed Implementation

[0056] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0057] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0058] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0059] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0060] Please see Figures 1-2 As shown, Figure 1 This is a flowchart of the dynamic control method for aeration volume in a wastewater treatment plant based on digital twins, as described in this invention. Figure 2 This is a flowchart illustrating the process of determining whether the current aeration control quality is up to standard in the dynamic control method for aeration volume in a wastewater treatment plant based on digital twins, as described in this invention.

[0061] The present invention provides a method for dynamic control of aeration volume in wastewater treatment plants based on digital twins, comprising:

[0062] Step S1: Construct a digital twin model of the wastewater treatment aeration process, collect the operating parameters of the wastewater treatment aeration process, input the operating parameters into the digital twin model, and obtain the predicted value of dissolved oxygen concentration.

[0063] Step S2: Obtain the preset target value of dissolved oxygen concentration and the measured value of dissolved oxygen concentration collected in real time, and calculate the dissolved oxygen deviation parameter based on the target value and the measured value;

[0064] Step S3: Based on the operating parameters, calculate the activity parameters characterizing the metabolic state of microorganisms, the mass transfer parameters characterizing the gas-liquid oxygen transfer efficiency, and the energy efficiency parameters characterizing the treatment effect per unit of energy consumption.

[0065] Step S4: Based on the dissolved oxygen deviation parameter, the activity parameter, the mass transfer parameter, and the energy efficiency parameter, determine the comprehensive judgment index characterizing the quality of aeration control;

[0066] Step S5: In response to the comprehensive judgment index being greater than the preset comprehensive judgment index, determine that the current aeration control quality is unqualified, and determine the reason for the unqualified aeration control quality based on the deviation of each parameter constituting the comprehensive judgment index.

[0067] Step S6: Determine the adjustment method of the operating parameters based on the reasons for the unqualified aeration control quality, and determine the adjustment amount in combination with the predicted value. Feed back the adjusted operating parameters to the digital twin model to update the digital twin model parameters.

[0068] In this embodiment of the invention, fluorescent dissolved oxygen sensors are deployed at the inlet, middle section, and outlet of the aeration tank to acquire dissolved oxygen concentration data at each monitoring point in the aeration tank in real time at a sampling frequency of once per second. A multi-parameter water quality analyzer is deployed at the inlet to acquire influent chemical oxygen demand, influent ammonia nitrogen concentration, influent flow rate, and water temperature data in real time at a sampling frequency of once per minute. A gas flow meter and power transmitter are deployed at the blower outlet manifold to acquire aeration volume, air supply pressure, and blower power consumption data in real time at a sampling frequency of once per second. Opening feedback sensors are deployed on the electric regulating valves of each aeration zone to acquire the opening data of each aeration disc in real time at a sampling frequency of five times per second. After outlier detection, missing value completion, data filtering, and spatiotemporal alignment preprocessing, the collected raw data forms an operating parameter set containing historical dissolved oxygen concentration sequences, influent chemical oxygen demand sequences, influent flow rate sequences, aeration volume sequences, water temperature sequences, and aeration disc opening sequence.

[0069] Specifically, in step S1, the predicted value of dissolved oxygen concentration is obtained, including:

[0070] A mechanistic model based on the mathematical model of activated sludge was established to describe the organic matter degradation, nitrification and denitrification reactions in the microbial metabolic process, and the first dissolved oxygen prediction value was output.

[0071] A data-driven model based on long short-term memory network is established, using historical dissolved oxygen concentration sequence, historical influent chemical oxygen demand sequence, historical influent flow rate sequence, historical aeration rate sequence and historical water temperature sequence as input features, and outputting a second dissolved oxygen prediction value.

[0072] The first and second predicted dissolved oxygen values ​​are fused together to output the predicted dissolved oxygen concentration.

[0073] In this embodiment of the invention, firstly, a mechanistic model based on the mathematical model of activated sludge is established. This mechanistic model focuses on the microbial metabolic process within the aeration tank, describing the variation of dissolved oxygen concentration through a dissolved oxygen kinetic equation. Specifically, the gas-liquid mass transfer coefficient is determined based on the ratio of aeration rate to aeration tank volume combined with an empirical correction coefficient; the saturated dissolved oxygen concentration is obtained from a table based on water temperature; the maximum specific growth rate, yield coefficient, half-saturation constant, and dissolved oxygen half-saturation constant adopt the standard recommended values ​​from the activated sludge mathematical model; and the activated sludge concentration is collected in real-time by a mixed liquor suspended solids concentration sensor. By solving this differential equation, the mechanistic model outputs a first predicted dissolved oxygen value.

[0074] Secondly, a data-driven model based on a Long Short-Term Memory (LSTM) network was established. This model uses historical dissolved oxygen concentration (DOC), influent chemical oxygen demand (COD), influent flow rate, aeration rate, and water temperature sequences from the past twelve time points as input feature vectors. Specifically, the historical DOC sequence was obtained by averaging the data acquired once per second by fluorescent dissolved oxygen sensors deployed at the inlet, middle section, and outlet of the aeration tank; the historical COD and influent flow rate sequences were acquired once per minute by a multi-parameter water quality analyzer deployed at the inlet; the historical aeration rate sequence was acquired once per second by a gas flow meter deployed at the blower outlet manifold; and the historical water temperature sequence was acquired once per minute by a temperature sensor deployed at the inlet. The LSM network structure consists of an input layer, a first LSM layer, a random deactivation layer, a second LSM layer, another random deactivation layer, a fully connected layer, and an output layer, outputting the second predicted DOC value for the next twelve time points. Finally, an adaptive weighted fusion method is used to fuse the first and second dissolved oxygen prediction values. The fusion weight coefficients are dynamically adjusted based on the historical prediction errors of the mechanistic model and the data-driven model over the past hour, assigning a larger fusion weight to the model with the smaller prediction error. Through this fusion process, the final predicted value of the dissolved oxygen concentration is output.

[0075] This invention constructs a digital twin system that integrates a fusion mechanism model and a data-driven model, and dynamically adjusts the prediction weights using an adaptive weighted fusion method. This achieves accurate prediction of dissolved oxygen concentration, overcomes the problem of insufficient prediction accuracy of a single model, and provides a reliable prediction basis for subsequent feedforward control of aeration volume.

[0076] Specifically, in step S2, the dissolved oxygen deviation parameter is calculated, including:

[0077] The measured dissolved oxygen concentration in real time is compared with the preset target dissolved oxygen concentration. The absolute value of the difference is then taken to obtain the dissolved oxygen deviation parameter.

[0078] In this embodiment of the invention, firstly, fluorescent dissolved oxygen sensors deployed at the inlet, middle section, and outlet of the aeration tank acquire the measured dissolved oxygen concentration values ​​at each monitoring point in real time at a sampling frequency of 1 second / time. The arithmetic mean of the measured values ​​at each monitoring point is then taken as the current measured dissolved oxygen concentration value of the aeration tank. Secondly, a preset target value for dissolved oxygen concentration is acquired. This target value is dynamically set according to the influent load and process status: when the influent chemical oxygen demand (COD) is ≤200 mg / L, the target value is set to 2.0~2.5 mg / L; when 200 mg / L < COD ≤ 500 mg / L, the target value is set to 2.5~3.0 mg / L; when COD > 500 mg / L, the target value is set to 3.0~4.0 mg / L; when sludge aging is detected, the target value is set to 1.5~2.0 mg / L; when filamentous bacterial bulking is detected, the target value is set to 2.5~3.5 mg / L. Finally, the difference between the real-time measured dissolved oxygen concentration and the dynamically set target dissolved oxygen concentration is calculated, and the absolute value of the difference is taken to obtain the dissolved oxygen deviation parameter. This dissolved oxygen deviation parameter is used to quantify the degree of deviation between the current dissolved oxygen concentration and the target value, serving as the basis for subsequent comprehensive judgment index calculations.

[0079] Specifically, in step S3, the activity parameters characterizing the metabolic state of microorganisms are calculated, including:

[0080] The concentration of volatile suspended solids and the dissolved oxygen consumption rate of the mixture are obtained from the operating parameters.

[0081] The specific oxygen consumption rate is calculated based on the concentration of volatile suspended solids in the mixture and the dissolved oxygen consumption rate.

[0082] A preset reference specific oxygen consumption rate is obtained, and the ratio of the specific oxygen consumption rate to the reference specific oxygen consumption rate is used as a microbial activity parameter.

[0083] In this embodiment of the invention, the concentration of volatile suspended solids (VSS) and the dissolved oxygen consumption rate of the mixed liquor are obtained from operating parameters. The VSS concentration is acquired in real time using a VSS sensor deployed in the aeration tank at a sampling frequency of 1 minute per acquisition. The dissolved oxygen consumption rate is determined by continuously collecting data on the change of dissolved oxygen concentration in the aeration tank over time, and measuring the rate of decrease in dissolved oxygen during brief intervals when aeration is stopped; this rate of decrease is the dissolved oxygen consumption rate. Next, the specific oxygen consumption rate is calculated based on the VSS concentration and dissolved oxygen consumption rate. The dissolved oxygen consumption rate is divided by the VSS concentration, and the resulting ratio is the specific oxygen consumption rate, expressed in milligrams of oxygen per gram of VSS per hour.

[0084] Next, a preset reference specific oxygen consumption rate (SOC) is obtained. This SOC is the baseline SOC under normal metabolic conditions, determined based on historical normal operating data of the wastewater treatment plant, and ranges from 8 to 12 mg of oxygen per gram of volatile suspended solids per hour. Finally, the ratio of the SOC to the reference SOC is used as a microbial activity parameter. A ratio greater than or equal to 0.9 indicates very high microbial activity; a ratio between 0.7 and 0.9 indicates normal microbial activity; a ratio between 0.5 and 0.7 indicates low microbial activity requiring increased aeration; and a ratio less than 0.5 indicates severely insufficient microbial activity requiring a significant increase in aeration.

[0085] Specifically, in step S3, the mass transfer parameters characterizing the gas-liquid oxygen transfer efficiency are calculated, including:

[0086] Obtain the aeration rate and current dissolved oxygen concentration from the operating parameters;

[0087] Obtain the preset saturated dissolved oxygen concentration and aeration tank volume, calculate the gas-liquid mass transfer coefficient based on the aeration rate, the saturated dissolved oxygen concentration, the current dissolved oxygen concentration and the aeration tank volume, and use the gas-liquid mass transfer coefficient as the mass transfer parameter.

[0088] In this embodiment of the invention, firstly, the aeration rate and current dissolved oxygen concentration are obtained from the operating parameters. The aeration rate is obtained in real time by a gas flow meter deployed at the blower outlet manifold at a sampling frequency of 1 second. The current dissolved oxygen concentration is obtained in real time by fluorescent dissolved oxygen sensors deployed at the inlet, middle section, and outlet of the aeration tank at a sampling frequency of 1 second, and the arithmetic mean of the measured values ​​at each monitoring point is taken as the current dissolved oxygen concentration of the aeration tank. Secondly, the preset saturated dissolved oxygen concentration and aeration tank volume are obtained. The saturated dissolved oxygen concentration is determined by referring to a table based on the current water temperature; for example, the saturated dissolved oxygen concentration is 9.08 mg / L at a water temperature of 20℃ and 8.38 mg / L at a water temperature of 25℃. The aeration tank volume is the design volume of the aeration tank, in cubic meters, and is determined according to the engineering design parameters of the wastewater treatment plant.

[0089] Then, the gas-liquid mass transfer coefficient is calculated based on the aeration rate, saturated dissolved oxygen concentration, current dissolved oxygen concentration, and aeration tank volume. The specific calculation method is as follows: First, calculate the natural logarithm of the ratio of the saturated dissolved oxygen concentration to the difference between the saturated dissolved oxygen concentration and the current dissolved oxygen concentration. Multiply this natural logarithm by the aeration rate and then divide by the aeration tank volume to obtain the gas-liquid mass transfer coefficient. The unit of the gas-liquid mass transfer coefficient is per hour. Finally, the calculated gas-liquid mass transfer coefficient is used as the mass transfer parameter. The gas-liquid mass transfer efficiency level is determined based on the value of the mass transfer parameter: when the mass transfer parameter is greater than or equal to 8 per hour, the mass transfer efficiency is very high; when the mass transfer parameter is between 5 and 8 per hour, the mass transfer efficiency is normal; when the mass transfer parameter is between 2 and 5 per hour, the mass transfer efficiency is low, and the aeration head blockage or blower condition needs to be checked; when the mass transfer parameter is less than 2 per hour, the mass transfer efficiency is severely insufficient, and emergency repairs or adjustments to the aeration strategy are required.

[0090] Specifically, in step S3, energy efficiency parameters characterizing the treatment effect per unit of energy consumption are calculated, including:

[0091] The influent chemical oxygen demand (COD), effluent COD, and aeration energy consumption are obtained from the aforementioned operating parameters.

[0092] The amount of chemical oxygen demand removed is calculated based on the difference between the influent chemical oxygen demand and the effluent chemical oxygen demand, and the ratio of the amount of chemical oxygen demand removed to the aeration energy consumption is used as an energy efficiency parameter.

[0093] In this embodiment of the invention, firstly, the influent chemical oxygen demand (COD), effluent COD, and aeration energy consumption are obtained from the operating parameters. Specifically, the influent COD is acquired in real-time using a multi-parameter water quality analyzer deployed at the inlet at a sampling frequency of 1 minute; the effluent COD is acquired in real-time using the same multi-parameter water quality analyzer deployed at the outlet at a sampling frequency of 1 minute; and the aeration energy consumption is acquired in real-time using a power transmitter deployed on the blower's power supply line at a sampling frequency of 1 second. The aeration energy consumption per unit time is calculated by integrating the power consumption per unit time, expressed in kilowatt-hours.

[0094] Secondly, the amount of chemical oxygen demand (COD) removed is calculated based on the difference between the influent COD and the effluent COD. The COD removed is calculated by subtracting the effluent COD from the influent COD per unit time; the difference is expressed as kilograms per hour. Then, the ratio of the removed COD to the aeration energy consumption is used as an energy efficiency parameter. This parameter is expressed in kilograms of COD per kilowatt-hour, representing the number of kilograms of COD removed per kilowatt-hour of electrical energy consumed. Energy efficiency levels are determined based on the values ​​of energy efficiency parameters: when the energy efficiency parameter is greater than or equal to 0.5 kg COD per kilowatt-hour, the energy efficiency is very high; when the energy efficiency parameter is between 0.3 and 0.5 kg COD per kilowatt-hour, the energy efficiency is normal; when the energy efficiency parameter is between 0.15 and 0.3 kg COD per kilowatt-hour, the energy efficiency is low; and when the energy efficiency parameter is less than 0.15 kg COD per kilowatt-hour, the energy efficiency is severely insufficient.

[0095] This invention quantifies microbial metabolic activity by calculating the ratio of specific oxygen consumption rate to a reference value, quantifies oxygen transfer efficiency by calculating the gas-liquid mass transfer coefficient, and quantifies the treatment effect per unit energy consumption by calculating the ratio of chemical oxygen demand removal to aeration energy consumption. This enables comprehensive perception and quantitative evaluation of the operating status of the aeration system, providing a data foundation for multi-parameter coordinated control.

[0096] Specifically, in step S4, a comprehensive judgment index characterizing the quality of aeration control is determined, including:

[0097] Obtain preset dissolved oxygen deviation threshold, activity parameter reference value, mass transfer parameter reference value and energy efficiency parameter reference value;

[0098] The ratio of the dissolved oxygen deviation parameter to the dissolved oxygen deviation threshold is calculated and used as the first deviation value.

[0099] Calculate the ratio of the value after subtracting the activity parameter from the reference value of the activity parameter, and use it as the second deviation value;

[0100] The ratio of the reference value of the mass transfer parameter to the mass transfer parameter is calculated and used as the third deviation value;

[0101] The ratio of the energy efficiency parameter reference value to the energy efficiency parameter is calculated and used as the fourth deviation value;

[0102] The first deviation value, the second deviation value, the third deviation value, and the fourth deviation value are squared, summed, and then the square root is taken to obtain the comprehensive judgment index.

[0103] In this embodiment of the invention, firstly, preset dissolved oxygen deviation threshold, activity parameter reference value, mass transfer parameter reference value, and energy efficiency parameter reference value are obtained. The dissolved oxygen deviation threshold is set to 2 mg / L, which serves as the benchmark for judging dissolved oxygen control quality in the aeration tank. The activity parameter reference value is set to 0.7, corresponding to the normal metabolic state when the volatile suspended solids concentration in the mixed liquor of the activated sludge process is 3000 to 4000 mg / L. The mass transfer parameter reference value is set to 5 / hour, which is the median of a good gas-liquid mass transfer efficiency range of 5 to 8 / hour. The energy efficiency parameter reference value is set to 0.3 kg COD / kWh, corresponding to the energy consumption efficiency calculated based on the industry standard that approximately 50 cubic meters of air are needed to degrade 1 kg of COD.

[0104] Next, four deviation values ​​are calculated. The first deviation value is the ratio of the dissolved oxygen deviation parameter to the dissolved oxygen deviation threshold, used to characterize the degree of deviation in dissolved oxygen concentration. The second deviation value is the ratio of 1 minus the activity parameter to the reference value of the activity parameter; that is, first subtracting the activity parameter from 1 to obtain the insufficient activity, then dividing by the reference value of the activity parameter, used to characterize the degree of insufficient microbial activity. The third deviation value is the ratio of the reference value of the mass transfer parameter to the mass transfer parameter itself, used to characterize the degree of deviation in gas-liquid mass transfer efficiency. The fourth deviation value is the ratio of the reference value of the energy efficiency parameter to the energy efficiency parameter itself, used to characterize the degree of deviation in energy consumption efficiency. Then, the first, second, third, and fourth deviation values ​​are squared respectively, the four squared values ​​are summed, and the square root of the sum is taken to obtain the comprehensive judgment index. This comprehensive judgment index is used to quantify the overall level of aeration control quality. The control level is determined based on the value of the comprehensive judgment index: when the comprehensive judgment index is less than or equal to 0.8, it indicates excellent aeration control quality; when the comprehensive judgment index is between 0.8 and 1.0, it indicates good aeration control quality; when the comprehensive judgment index is between 1.0 and 1.3, it indicates that partial adjustments are needed; when the comprehensive judgment index is greater than 1.3, it indicates that comprehensive adjustments are needed, i.e., the aeration control quality is unqualified.

[0105] This invention constructs a comprehensive quantitative evaluation model for aeration control quality by normalizing and weighting the deviation values ​​of four dimensions: dissolved oxygen deviation, microbial activity, gas-liquid mass transfer efficiency, and energy consumption efficiency. This model enables a comprehensive evaluation of the single-index operation status of the aeration system and provides a unified judgment benchmark for non-compliance diagnosis and control decisions.

[0106] Specifically, in step S5, the reasons for the unqualified aeration control quality are determined, including:

[0107] The normalized squared value of the dissolved oxygen deviation parameter is calculated as the first contribution.

[0108] The sum of squares of the deviations of the activity parameter, mass transfer parameter, and energy efficiency parameter is calculated as the second contribution.

[0109] Compare the magnitudes of the first contribution and the second contribution;

[0110] If the first contribution is greater than the second contribution, the reason for non-compliance is determined to be the aeration volume deviation category;

[0111] If the first contribution is not greater than the second contribution, the reason for non-compliance is determined to be the active mass transfer category.

[0112] In this embodiment of the invention, firstly, when the comprehensive judgment index is greater than 1.3, the current aeration control quality is determined to be unqualified, and the process of diagnosing the cause of unqualification is initiated. Secondly, the first contribution is calculated. The dissolved oxygen deviation parameter obtained in step S2 is normalized and squared, that is, the ratio of the dissolved oxygen deviation parameter to the dissolved oxygen deviation threshold is calculated and then squared to obtain the first contribution. This first contribution is used to quantify the degree of contribution of dissolved oxygen deviation to the unqualified aeration control quality. Then, the second contribution is calculated. The second deviation value, the third deviation value, and the fourth deviation value calculated in step S4 are squared respectively, and the three squared values ​​are summed to obtain the second contribution. Among them, the second deviation value is the deviation value of the activity parameter, the third deviation value is the deviation value of the mass transfer parameter, and the fourth deviation value is the deviation value of the energy efficiency parameter. This second contribution is used to quantify the combined contribution of the three factors of microbial activity, gas-liquid mass transfer efficiency, and energy consumption efficiency to the unqualified aeration control quality. Finally, the magnitudes of the first contribution and the second contribution are compared. If the first contribution is greater than the second contribution, it indicates that dissolved oxygen deviation is the main factor causing the aeration control quality to be unqualified, and the reason for unqualification is determined to be the aeration volume deviation category; if the first contribution is not greater than the second contribution, it indicates that problems with microbial activity, gas-liquid mass transfer efficiency, and energy consumption efficiency are the main factors causing the aeration control quality to be unqualified, and the reason for unqualification is determined to be the activity mass transfer category.

[0113] This invention constructs a two-factor diagnostic model for the causes of non-compliance by comparing the contribution of dissolved oxygen deviation with the combined contribution of activity, mass transfer, and energy efficiency. When the contribution of dissolved oxygen deviation is greater than the combined contribution of the three factors, it indicates that the mismatch between aeration supply and demand is the dominant factor leading to non-compliance in quality control, thus classifying it as an aeration deviation category. Conversely, if the contribution is less than the combined contribution, it indicates that insufficient microbial metabolic activity, low gas-liquid oxygen transfer efficiency, or poor energy consumption treatment effect are the main causes, classifying it as an activity-mass transfer category. This diagnostic method simplifies a complex multi-parameter coupling problem into a single-factor comparison, achieving rapid and accurate classification of the causes of non-compliance.

[0114] Specifically, in step S6, the adjustment method of the operating parameters is determined based on the reason for the unqualified aeration control quality, and the adjustment amount is determined in conjunction with the predicted value, including:

[0115] When the reason for non-compliance is the aeration volume deviation category, the blower frequency adjustment amount is calculated based on the dissolved oxygen deviation parameter and the predicted value, and the blower operating frequency is adjusted according to the blower frequency adjustment amount.

[0116] When the reason for non-compliance is the activity mass transfer category, the aeration disc opening adjustment amount is calculated based on the activity parameters and the mass transfer parameters, and the aeration disc opening is adjusted according to the aeration disc opening adjustment amount.

[0117] In this embodiment of the invention, the adjustment process when the cause of non-compliance is aeration volume deviation involves first obtaining the current dissolved oxygen deviation parameter and the predicted dissolved oxygen concentration value. The dissolved oxygen deviation parameter reflects the degree and direction of deviation between the current measured dissolved oxygen concentration and the target concentration; if the measured value is greater than the target value, the deviation is positive; if the measured value is less than the target value, the deviation is negative. The predicted dissolved oxygen concentration value reflects the trend of dissolved oxygen change over the next hour; if the predicted value shows a decreasing trend, the future oxygen demand may increase; if the predicted value shows an increasing trend, the future oxygen demand may decrease.

[0118] Secondly, determine the basis for adjusting the blower frequency. The adjustment basis is the sign and magnitude of the dissolved oxygen deviation parameter, combined with the trend of the predicted value: When the dissolved oxygen deviation parameter is positive and the predicted value is rising, it indicates that the current dissolved oxygen is too high and will continue to rise in the future, requiring a significant reduction in the blower frequency; when the dissolved oxygen deviation parameter is positive but the predicted value is falling, it indicates that although the current level is too high, it will naturally decrease in the future, requiring only a slight reduction in the blower frequency or no adjustment; when the dissolved oxygen deviation parameter is negative and the predicted value is falling, it indicates that the current dissolved oxygen is too low and will continue to decrease in the future, requiring a significant increase in the blower frequency; when the dissolved oxygen deviation parameter is negative but the predicted value is rising, it indicates that although the current level is too low, it will naturally recover in the future, requiring only a slight increase in the blower frequency or no adjustment.

[0119] Next, the blower frequency adjustment is calculated. The specific calculation process is as follows: Preset values ​​for the upper limit of dissolved oxygen (ALO) are 6 mg / L, the lower limit is 1 mg / L, the maximum blower frequency is 50 Hz, and the frequency adjustment coefficient is a dynamic value between 0.8 and 1.2. First, the ratio of the ABO deviation parameter to the ABO variation range is calculated, i.e., the ABO deviation parameter is divided by the difference between the upper and lower limits of ABO (5 mg / L), yielding the normalized deviation. This normalized deviation is multiplied by the maximum blower frequency of 50 Hz to obtain the basic adjustment. Then, the frequency adjustment coefficient is adjusted according to the predicted trend. If the predicted trend is consistent with the deviation direction, a larger coefficient of 1.2 is used; if the predicted trend is opposite to the deviation direction, a smaller coefficient of 0.8 is used; if the trend is not obvious, a coefficient of 1.0 is used. The basic adjustment is multiplied by the frequency adjustment coefficient to obtain the blower frequency adjustment. A positive adjustment increases the frequency, and a negative adjustment decreases the frequency. Finally, the blower frequency adjustment is executed. The calculated blower frequency adjustment amount is sent to the blower frequency converter. The controller gradually adjusts the blower operating frequency according to the adjustment amount, with each adjustment increment not exceeding 2 Hz to avoid sudden changes in air supply that could impact the biochemical system. After adjustment, the aeration rate is re-measured using a gas flow meter to confirm the adjustment effect.

[0120] In this embodiment of the invention, the adjustment process when the cause of non-compliance is related to the activity and mass transfer category involves the following steps: First, the current activity parameters and mass transfer parameters are obtained. The activity parameters reflect the metabolic activity state of the microorganisms; the lower the value, the worse the activity. The mass transfer parameters reflect the gas-liquid oxygen transfer efficiency; the lower the value, the more likely there is blockage in the aeration disc or uneven bubble distribution. Second, the basis for adjusting the aeration disc opening is determined. The adjustment basis is the difference between the activity parameters and the reference value of the activity parameters, combined with the mass transfer parameters for comprehensive judgment. When the activity parameters are low but the mass transfer parameters are normal, it indicates that the increased oxygen demand of the microorganisms is due to insufficient metabolic activity, requiring an increase in aeration to maintain the dissolved oxygen level, and the aeration disc opening should be increased. When the activity parameters are normal but the mass transfer parameters are low, it indicates that the decreased oxygen transfer efficiency is due to blockage or aging of the aeration discs, requiring an increase in the aeration disc opening to compensate for the loss of mass transfer efficiency, and maintenance and cleaning should be arranged. When both the activity parameters and the mass transfer parameters are low, it indicates that there are problems in both aspects, requiring a significant increase in the aeration disc opening and the initiation of maintenance procedures.

[0121] Next, the aeration disc opening adjustment amount is calculated. The specific calculation process is as follows: A preset activity parameter reference value of 0.7 and an opening adjustment coefficient ranging from 0.5 to 1.5 are obtained. First, the activity deficiency is calculated by subtracting the current activity parameter from the value 1; this deficiency is then divided by the activity parameter reference value of 0.7 to obtain the normalized activity deviation value; this is then multiplied by the opening adjustment coefficient to obtain the basic opening adjustment amount. Based on this, corrections are made according to the mass transfer parameter: the ratio of the hourly mass transfer parameter reference value of 5 to the current mass transfer parameter is calculated, and this ratio is used as the mass transfer correction coefficient; the basic opening adjustment amount is multiplied by the mass transfer correction coefficient to obtain the final aeration disc opening adjustment amount. A positive adjustment amount opens the aeration disc wider, while a negative adjustment amount closes it narrower. The maximum opening of the aeration disc is limited to 80%, with a 20% margin reserved to prevent over-aeration.

[0122] Finally, the aeration disc opening is adjusted. In a multi-stage aeration tank, the aeration volume needs to be dynamically allocated based on the dissolved oxygen deviation distribution in each aeration zone. The specific allocation process is as follows: Dissolved oxygen concentrations in each aeration zone are monitored in real time using dissolved oxygen sensors deployed in each zone. The absolute value of the deviation between the dissolved oxygen concentration in each zone and the target value is calculated. The aeration volume allocation coefficient for each zone is determined based on the proportion of the absolute value of the deviation in each zone to the sum of all absolute values ​​of deviation. The total aeration volume adjustment is multiplied by the allocation coefficient for each zone to obtain the aeration volume adjustment for each zone, which is then converted into the opening adjustment for each aeration disc. The opening adjustment for each aeration disc is sent to the corresponding electric regulating valve controller. The aeration disc opening is gradually adjusted according to the adjustment amount, with each adjustment increment not exceeding five percent to avoid sudden changes in aeration volume. After adjustment, the dissolved oxygen concentration in each aeration zone is re-measured using dissolved oxygen sensors to verify the adjustment effect.

[0123] Specifically, in step S6, the parameters of the digital twin model are updated, including:

[0124] The measured value of the adjusted dissolved oxygen concentration is used as a feedback signal, and the prediction error between the feedback signal and the predicted value of the dissolved oxygen concentration before adjustment is calculated.

[0125] The fusion weights of the mechanistic model and the data-driven model in the digital twin model are dynamically adjusted based on the prediction error to complete the parameter update of the digital twin model.

[0126] In this embodiment of the invention, after the above adjustments are completed, the adjusted operating parameters and the newly collected measured dissolved oxygen concentration values ​​are fed back to the digital twin model. The digital twin model compares the adjusted measured dissolved oxygen concentration values ​​with the predicted values ​​before adjustment, calculates the prediction error, and dynamically adjusts the fusion weight coefficients of the mechanistic model and the data-driven model based on the prediction error to complete the model parameter update. The updated model re-outputs the predicted dissolved oxygen concentration value and enters the next round of regulation cycle until the comprehensive judgment index drops below the qualified threshold of 1.3.

[0127] This invention distinguishes between two causes of non-compliance and adopts differentiated adjustment strategies. In the adjustment of aeration volume deviation, it introduces predicted values ​​for feedforward control to overcome the lag problem of traditional feedback control. In the adjustment of activity and mass transfer, it introduces activity parameters and mass transfer parameters for synergistic optimization and adopts a dynamic allocation strategy of multiple aeration zones, thereby achieving precise, efficient and dynamic control of aeration volume.

[0128] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0129] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dynamic control of aeration volume in a wastewater treatment plant based on digital twins, characterized in that, include: Step S1: Construct a digital twin model of the wastewater treatment aeration process, collect the operating parameters of the wastewater treatment aeration process, input the operating parameters into the digital twin model, and obtain the predicted value of dissolved oxygen concentration. Step S2: Obtain the preset target value of dissolved oxygen concentration and the measured value of dissolved oxygen concentration collected in real time, and calculate the dissolved oxygen deviation parameter based on the target value and the measured value; Step S3: Based on the operating parameters, calculate the activity parameters characterizing the metabolic state of microorganisms, the mass transfer parameters characterizing the gas-liquid oxygen transfer efficiency, and the energy efficiency parameters characterizing the treatment effect per unit of energy consumption. Step S4: Based on the dissolved oxygen deviation parameter, the activity parameter, the mass transfer parameter, and the energy efficiency parameter, determine the comprehensive judgment index characterizing the quality of aeration control; Step S5: In response to the comprehensive judgment index being greater than the preset comprehensive judgment index, determine that the current aeration control quality is unqualified, and determine the reason for the unqualified aeration control quality based on the deviation of each parameter constituting the comprehensive judgment index. Step S6: Determine the adjustment method of the operating parameters based on the reasons for the unqualified aeration control quality, and determine the adjustment amount in combination with the predicted value. Feed back the adjusted operating parameters to the digital twin model to update the digital twin model parameters.

2. The method for dynamic control of aeration volume in a wastewater treatment plant based on digital twins according to claim 1, characterized in that, In step S1, the predicted value of dissolved oxygen concentration is obtained, including: A mechanistic model based on the mathematical model of activated sludge was established to describe the organic matter degradation, nitrification and denitrification reactions in the microbial metabolic process, and the first dissolved oxygen prediction value was output. A data-driven model based on long short-term memory network is established, using historical dissolved oxygen concentration sequence, historical influent chemical oxygen demand sequence, historical influent flow rate sequence, historical aeration rate sequence and historical water temperature sequence as input features, and outputting a second dissolved oxygen prediction value. The first and second predicted dissolved oxygen values ​​are fused together to output the predicted dissolved oxygen concentration.

3. The method for dynamic control of aeration volume in a wastewater treatment plant based on digital twins according to claim 1, characterized in that, In step S2, the dissolved oxygen deviation parameter is calculated, including: The measured dissolved oxygen concentration in real time is compared with the preset target dissolved oxygen concentration. The absolute value of the difference is then taken to obtain the dissolved oxygen deviation parameter.

4. The method for dynamic control of aeration volume in a wastewater treatment plant based on digital twins according to claim 1, characterized in that, In step S3, activity parameters characterizing the metabolic state of microorganisms are calculated, including: The concentration of volatile suspended solids and the dissolved oxygen consumption rate of the mixture are obtained from the operating parameters. The specific oxygen consumption rate is calculated based on the concentration of volatile suspended solids in the mixture and the dissolved oxygen consumption rate. A preset reference specific oxygen consumption rate is obtained, and the ratio of the specific oxygen consumption rate to the reference specific oxygen consumption rate is used as a microbial activity parameter.

5. The method for dynamic control of aeration volume in a wastewater treatment plant based on digital twins according to claim 1, characterized in that, In step S3, the mass transfer parameters characterizing the gas-liquid oxygen transfer efficiency are calculated, including: Obtain the aeration rate and current dissolved oxygen concentration from the operating parameters; Obtain the preset saturated dissolved oxygen concentration and aeration tank volume, calculate the gas-liquid mass transfer coefficient based on the aeration rate, the saturated dissolved oxygen concentration, the current dissolved oxygen concentration and the aeration tank volume, and use the gas-liquid mass transfer coefficient as the mass transfer parameter.

6. The method for dynamic control of aeration volume in a wastewater treatment plant based on digital twins according to claim 1, characterized in that, In step S3, energy efficiency parameters characterizing the treatment effect per unit of energy consumption are calculated, including: The influent chemical oxygen demand (COD), effluent COD, and aeration energy consumption are obtained from the aforementioned operating parameters. The amount of chemical oxygen demand removed is calculated based on the difference between the influent chemical oxygen demand and the effluent chemical oxygen demand, and the ratio of the amount of chemical oxygen demand removed to the aeration energy consumption is used as an energy efficiency parameter.

7. The method for dynamic control of aeration volume in a wastewater treatment plant based on digital twins according to claim 1, characterized in that, In step S4, a comprehensive judgment index characterizing the quality of aeration control is determined, including: Obtain preset dissolved oxygen deviation threshold, activity parameter reference value, mass transfer parameter reference value and energy efficiency parameter reference value; The ratio of the dissolved oxygen deviation parameter to the dissolved oxygen deviation threshold is calculated and used as the first deviation value. Calculate the ratio of the value after subtracting the activity parameter from the reference value of the activity parameter, and use it as the second deviation value; The ratio of the reference value of the mass transfer parameter to the mass transfer parameter is calculated and used as the third deviation value; The ratio of the energy efficiency parameter reference value to the energy efficiency parameter is calculated and used as the fourth deviation value; The first deviation value, the second deviation value, the third deviation value, and the fourth deviation value are squared, summed, and then the square root is taken to obtain the comprehensive judgment index.

8. The method for dynamic control of aeration volume in a wastewater treatment plant based on digital twins according to claim 1, characterized in that, In step S5, the reasons for the unqualified aeration control quality are determined, including: The normalized squared value of the dissolved oxygen deviation parameter is calculated as the first contribution. The sum of squares of the deviations of the activity parameter, mass transfer parameter, and energy efficiency parameter is calculated as the second contribution. Compare the magnitudes of the first contribution and the second contribution; If the first contribution is greater than the second contribution, the reason for non-compliance is determined to be the aeration volume deviation category; If the first contribution is not greater than the second contribution, the reason for non-compliance is determined to be the active mass transfer category.

9. The method for dynamic control of aeration volume in a wastewater treatment plant based on digital twins according to claim 8, characterized in that, In step S6, the adjustment method of the operating parameters is determined based on the reason for the unqualified aeration control quality, and the adjustment amount is determined in conjunction with the predicted value, including: When the reason for non-compliance is the aeration volume deviation category, the blower frequency adjustment amount is calculated based on the dissolved oxygen deviation parameter and the predicted value, and the blower operating frequency is adjusted according to the blower frequency adjustment amount. When the reason for non-compliance is the activity mass transfer category, the aeration disc opening adjustment amount is calculated based on the activity parameters and the mass transfer parameters, and the aeration disc opening is adjusted according to the aeration disc opening adjustment amount.

10. The method for dynamic control of aeration volume in a wastewater treatment plant based on digital twins according to claim 1, characterized in that, In step S6, the parameters of the digital twin model are updated, including: The measured value of the adjusted dissolved oxygen concentration is used as a feedback signal, and the prediction error between the feedback signal and the predicted value of the dissolved oxygen concentration before adjustment is calculated. The fusion weights of the mechanistic model and the data-driven model in the digital twin model are dynamically adjusted based on the prediction error to complete the parameter update of the digital twin model.

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