Dissolved oxygen control method and system for aeration system

Abstract

The present invention relates to the technical field of sewage treatment, and in particular to a dissolved oxygen control method and system for an aeration system, comprising: obtaining water quality parameter information of an aeration tank inlet and an aeration tank inlet based on a preset data acquisition frequency, and arranging at least two sets of water quality parameter information in a time series to obtain a time series water quality feature set; performing a dynamic oxygen demand assessment on the time series water quality feature set to obtain a dynamic dissolved oxygen range; dividing a plurality of time windows according to an initial sewage treatment control instruction, and determining a target dissolved oxygen range and an aeration energy consumption constraint within each time window; calculating a real-time dissolved oxygen gap between the dynamic dissolved oxygen range and the target dissolved oxygen range within the corresponding time window; coupling analysis of the real-time dissolved oxygen gap and the aeration energy consumption constraint to obtain real-time aeration control parameters, and adjusting the aeration system accordingly; the method can avoid under-aeration or over-aeration problems caused by hysteresis adjustment, and reduce energy waste caused by frequent overshoot.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to a dissolved oxygen control method and system applied to an aeration system. Background Art

[0002] In the activated sludge wastewater treatment process, the aeration system is a key link in maintaining the normal progress of biochemical reactions. Its main function is to provide the oxygen required by microorganisms in the aeration tank to decompose organic matter. At the same time, it has a stirring and mixing effect to ensure that microorganisms are in full contact with wastewater. The precise control of dissolved oxygen (DO) is directly related to the efficiency, energy consumption and effluent quality of wastewater treatment.

[0003] Existing dissolved oxygen control methods usually adopt fixed dissolved oxygen set values ​​or feedback adjustment based on instantaneous water quality parameters, resulting in the control instructions lagging behind the actual water quality fluctuations. Specifically, when the influent load changes continuously, the fixed dissolved oxygen set value cannot match the dynamic demand. For example, during the period when the influent ammonia nitrogen concentration continues to rise, the oxygen demand for nitrification reaction accumulates and increases accordingly. However, because the existing methods only rely on the dissolved oxygen measurement value at the current moment for adjustment, they cannot predict the oxygen demand trend in subsequent time periods, resulting in the actual dissolved oxygen concentration being lower than the demand threshold for a long time, forcing the aeration system to compensate for the under-aeration with ultra-high energy consumption during the lag stage, resulting in energy waste or reduced treatment effect. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a dissolved oxygen control method and system for an aeration system, which can avoid underexposure or overexposure problems caused by hysteresis adjustment, while reducing energy waste caused by frequent overshoot.

[0005] In a first aspect, the present invention provides a dissolved oxygen control method for an aeration system, comprising:

[0006] Based on the preset data acquisition frequency, the water quality parameter information of the aeration tank inlet and the tank is obtained, and at least two sets of water quality parameter information are arranged in time sequence to obtain a time series water quality feature set;

[0007] Performing a dynamic oxygen demand assessment on the time series water quality feature set to obtain a dynamic dissolved oxygen range;

[0008] Based on the initial sewage treatment control instructions, multiple time windows are divided and the target dissolved oxygen range and aeration energy consumption constraints within each time window are determined;

[0009] Calculating a real-time dissolved oxygen gap between the dynamic dissolved oxygen range and the target dissolved oxygen range within a corresponding time window;

[0010] The real-time aeration control parameters are obtained by coupling analysis of the dissolved oxygen real-time gap and the aeration energy consumption constraint, and the aeration system is adjusted accordingly.

[0011] Furthermore, the water quality parameter information includes chemical oxygen demand, ammonia nitrogen content, mixed liquid suspended matter concentration and water temperature.

[0012] Furthermore, the aeration control parameters include fan frequency, valve opening and sludge return ratio.

[0013] Furthermore, a dynamic oxygen demand assessment is performed on the time series water quality feature set, including:

[0014] Calculate the oxygen demand for organic matter degradation based on the degradation kinetics model of chemical oxygen demand;

[0015] Calculate the oxygen demand during the nitrification stage based on the accumulated ammonia nitrogen and the temperature correction factor;

[0016] The oxygen demand of organic matter degradation and the oxygen demand of nitrification stage are superimposed, and the inhibition factor of mixed liquor suspended matter concentration on oxygen transfer efficiency is introduced to obtain the dynamic oxygen demand intensity.

[0017] Furthermore, the calculation formula for the oxygen demand for organic matter degradation is:

[0018] ThOD_COD=COD×(1-η)×α;

[0019] Where ThOD_COD represents the amount of oxygen required to degrade organic matter in the influent; COD represents the measured concentration of chemical oxygen demand in the influent; η represents the COD removal efficiency; and α is the oxygen conversion coefficient, which represents the amount of oxygen required to completely oxidize a unit mass of COD.

[0020] Furthermore, the calculation formula for the oxygen demand in the nitrification stage is:

[0021] ThOD_NH3=∫NH3-N×4.57×(1+0.03×(T-20));

[0022] Where ThOD_NH3 represents the oxygen demand during the nitrification stage; ∫NH3-N is the integral of the influent ammonia nitrogen concentration within the time window, indicating the cumulative amount of ammonia nitrogen; 4.57 is the theoretical oxygen demand coefficient for complete nitrification of ammonia nitrogen; T represents the water temperature in the aeration tank; and 0.03 is the temperature correction factor, indicating the adjustment ratio of the oxygen demand for every 1°C deviation of the temperature from the base temperature.

[0023] Furthermore, the aeration energy consumption constraints include a fan frequency range, a valve opening range, and a unit water energy consumption target.

[0024] On the other hand, the present application also provides a dissolved oxygen control system for an aeration system, the system comprising:

[0025] A data processing module is used to collect water quality parameter information of the aeration tank inlet and the tank based on a preset data acquisition frequency, and to arrange at least two sets of water quality parameter information in time series to generate a time series water quality feature set;

[0026] Dynamic oxygen demand assessment module, used to perform dynamic oxygen demand assessment on the time series water quality feature set to obtain the dynamic dissolved oxygen range;

[0027] The target parameter determination module is used to divide multiple time windows according to the initial sewage treatment control instructions, and determine the corresponding target dissolved oxygen range and aeration energy consumption constraint for each time window;

[0028] Gap calculation module, used to calculate the real-time dissolved oxygen gap between the dynamic dissolved oxygen range and the target dissolved oxygen range in the corresponding time window;

[0029] The control module is used to couple the dissolved oxygen real-time gap and aeration energy consumption constraints to obtain real-time aeration control parameters and adjust the aeration system accordingly.

[0030] In a third aspect, the present application provides an electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program implements the steps of any one of the above methods when executed by the processor.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in any one of the above methods when executed by a processor.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] Existing methods rely on fixed dissolved oxygen set points or single-point data adjustments at the current moment, failing to capture the temporal trends of water quality parameters. This method, by constructing a time-series water quality feature set and incorporating at least two sets of historical data into the analysis, can identify the dynamic evolution of water quality parameters, upgrading dissolved oxygen control from immediate response to trend prediction, allowing for early perception of the direction of oxygen demand changes and avoiding under- or over-exposure problems caused by delayed regulation. The dissolved oxygen range generated by dynamic oxygen demand assessment, rather than a fixed value, can match the changes in oxygen demand intensity under water quality fluctuations in real time, avoiding the extensive regulation mode of compensating for non-compliance under fixed thresholds. This allows the aeration system to maintain a smooth response during gradual water quality changes and reduces energy waste caused by frequent overshoots.

[0034] Based on the initial control instructions, time windows are divided and independent target dissolved oxygen ranges and energy consumption constraints are set for each window, breaking down the long-term sewage treatment goal into short-term controllable sub-goals. For example, during the time window of peak influent load, a slightly higher dissolved oxygen target is allowed to ensure treatment effectiveness, while over-aeration is avoided through energy consumption constraints. During low-load periods, the dissolved oxygen target is lowered and energy consumption is strictly limited, achieving precise control in different time periods and avoiding the one-size-fits-all regulation in existing methods that ignores the balance between energy consumption and effect.

[0035] By calculating the real-time gap between the dynamic dissolved oxygen range and the target dissolved oxygen range and performing a coupling analysis based on energy consumption constraints, the generation of aeration control parameters can simultaneously meet the dual goals of oxygen demand compensation and optimal energy consumption, thereby minimizing energy consumption while ensuring treatment effects, avoiding the single-target adjustment defect of existing methods that ignores costs to achieve the target. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flow chart of the dissolved oxygen control method applied to the aeration system of the present invention;

[0037] Figure 2 It is a structural diagram of the dissolved oxygen control system applied to the aeration system in the present invention. DETAILED DESCRIPTION

[0038] The present application is described below in conjunction with the accompanying drawings.

[0039] like Figure 1 As shown, the dissolved oxygen control method applied to the aeration system of the present invention specifically includes the following steps:

[0040] Step S1: Based on a preset data acquisition frequency, obtain water quality parameter information of the aeration tank inlet and the tank, and arrange at least two sets of water quality parameter information in time sequence to obtain a time series water quality feature set;

[0041] Step S2: performing a dynamic oxygen demand assessment on the time series water quality feature set to obtain a dynamic dissolved oxygen range;

[0042] Step S3: Divide multiple time windows according to the initial sewage treatment control instructions, and determine the target dissolved oxygen range and aeration energy consumption constraint within each time window;

[0043] Step S4, calculating the real-time dissolved oxygen gap between the dynamic dissolved oxygen range and the target dissolved oxygen range within the corresponding time window;

[0044] Step S5: Coupling analysis is performed on the real-time dissolved oxygen gap and the aeration energy consumption constraint to obtain real-time aeration control parameters, and the aeration system is adjusted accordingly.

[0045] In this embodiment, by constructing a time-series water quality feature set and incorporating at least two sets of historical data into the analysis, the dynamic evolution of water quality parameters can be identified, enabling the upgraded dissolved oxygen control from immediate response to trend prediction. This allows for early perception of the direction of oxygen demand changes, thus avoiding under- or over-exposure issues caused by delayed regulation. The dissolved oxygen range generated by dynamic oxygen demand assessment, rather than a fixed value, allows for real-time matching of oxygen demand intensity changes under water quality fluctuations, avoiding the extensive regulation mode of compensating for non-compliance under fixed thresholds. This allows the aeration system to maintain a smooth response during gradual water quality changes, reducing energy waste caused by frequent overshoots.

[0046] Based on the initial control instructions, time windows are divided and independent target dissolved oxygen ranges and energy consumption constraints are set for each window, breaking down the long-term sewage treatment goal into short-term controllable sub-goals. For example, during the time window of peak influent load, a slightly higher dissolved oxygen target is allowed to ensure treatment effectiveness, while over-aeration is avoided through energy consumption constraints. During low-load periods, the dissolved oxygen target is lowered and energy consumption is strictly limited, achieving precise control in different time periods and avoiding the one-size-fits-all regulation in existing methods that ignores the balance between energy consumption and effect.

[0047] By calculating the real-time gap between the dynamic dissolved oxygen range and the target dissolved oxygen range and performing a coupled analysis based on energy consumption constraints, the generation of aeration control parameters can simultaneously meet the dual goals of oxygen demand compensation and energy consumption optimization. This minimizes energy consumption while ensuring treatment results, avoiding the single-target adjustment flaw of existing methods that ignores cost to achieve the target.

[0048] In terms of overall logic, this method constructs a "data-driven + goal-oriented" active control system through the logical chain of "time series data modeling → dynamic demand forecasting → time window planning → coupled optimization decision-making." For example, the time series water quality feature set provides a trend basis for dynamic oxygen demand assessment, the time window transforms long-term control goals into executable short-term strategies, and the coupled analysis of dissolved oxygen gap and energy consumption constraints ensures that each short-term strategy is implemented under the "effect-energy consumption" balance, forming a closed-loop optimization from data perception to execution and regulation, significantly improving the system's adaptability to continuous load changes, such as avoiding long-term underexposure caused by continuously elevated ammonia nitrogen in the influent.

[0049] Through multi-dimensional data fusion and constraint coupling, energy consumption and treatment effect are optimized in a coordinated manner. Dynamic oxygen demand assessment determines the DO range based on actual water quality requirements, avoiding ineffective aeration caused by excessive safety margins, such as the traditional method of setting excessively high fixed DO values ​​to cope with load fluctuations. Time window division enables the system to allocate resources during different time periods, such as proactively lowering the DO target and limiting energy consumption during low-load periods at night. Calculation of the dissolved oxygen gap ensures that the adjustment amount within each window accurately matches actual demand, avoiding energy consumption surges caused by overcompensation, such as the existing method that requires ultra-high aeration rates to compensate for low oxygen conditions due to delayed adjustment.

[0050] In summary, this embodiment forms an intelligent control system that adapts to dynamic changes in water quality through three-layer logical coordination of time series characteristics-driven demand forecasting, time window decomposition of control targets, and coupled analysis and balancing of multiple constraints. While avoiding the lag and extensive defects of traditional methods, it achieves in-depth optimization of sewage treatment efficiency and energy consumption, and is particularly suitable for complex working conditions with frequent fluctuations in inlet load.

[0051] In some embodiments, the data collection scope covers key water quality parameters of the aeration tank inlet and the mixed liquor in the tank; the inlet parameters include chemical oxygen demand (COD) and ammonia nitrogen content (NH3-N), both of which directly reflect the inlet load: COD represents the concentration of organic matter, and an increase in its value indicates an increase in the oxygen demand for microbial decomposition of organic matter; an increase in ammonia nitrogen content indicates an increase in the potential oxygen demand for nitrification, because the process of converting ammonia nitrogen into nitrate consumes a large amount of dissolved oxygen; the parameters in the tank include mixed liquor suspended solids concentration (MLSS) and water temperature, among which MLSS reflects the total amount of microorganisms in the activated sludge, and an increase in its value means that the number of microorganisms participating in the biochemical reaction increases, and the overall oxygen demand increases synchronously with the metabolic activity; the water temperature affects the activity of microbial enzymes and the solubility of oxygen in water. Temperature changes will directly change the biochemical reaction rate and dissolved oxygen demand. For example, for every 10°C increase in temperature, the microbial metabolic rate increases by about 1 times, and the oxygen demand increases accordingly.

[0052] At the same time, the preset data acquisition frequency is set to minute level, such as once every 5 to 15 minutes, to match the dynamic characteristics of the sewage treatment process; the influent load may change significantly in a short period of time due to factors such as industrial drainage and water consumption peaks. High-frequency acquisition can timely capture sudden fluctuations in parameters such as COD and ammonia nitrogen, providing data support for the system to respond in advance; although the biochemical reactions in the pool are relatively slow, the cumulative effect of microbial metabolism needs to be identified through continuous data monitoring. For example, the continuous increase in ammonia nitrogen concentration indicates that the nitrification reaction is gradually intensifying, and the oxygen demand will increase over time; online monitoring equipment such as COD sensors, ammonia nitrogen electrodes, MLSS detectors and temperature probes collect data in real time to ensure the timeliness of the information; at the same time, sensors need to be calibrated regularly to avoid drift errors affecting subsequent analysis and ensure the reliability of the data.

[0053] Furthermore, at least two groups are arranged in timestamp order to form a dynamic data set containing historical and current status; for example, collecting data for the past two hours at intervals of 10 minutes can generate 12 groups of time series data, each group covering four characteristics: COD, NH3-N, MLSS, and temperature. The time series arrangement not only records the current water quality status, but also reflects the parameter change trend through the data sequence, such as the ammonia nitrogen concentration rising for three consecutive cycles and the COD fluctuation amplitude gradually narrowing; trend information can identify the direction (such as increasing or decreasing) and rate (change slope) of load changes, providing input for subsequent dynamic oxygen demand assessment; for example, the influent COD continues to rise from 300 mg / L to 500 mg / L, indicating that the organic load has increased sharply and the oxygen demand peak is about to come; the MLSS in the pool is stable at around 3000 mg / L, indicating that the microbial concentration is in a steady state and the oxygen demand fluctuation is mainly driven by the influent substrate load.

[0054] In this embodiment, a basic data set reflecting the dynamic changes in water quality is constructed through multi-dimensional parameter collection and time series processing, effectively solving the lag problem of existing methods that only rely on single-point data at the current moment; the time series feature set contains the historical evolution information of the parameters, enabling the system to capture the continuous change trend of the influent load and the working conditions in the pool, rather than the isolated instantaneous state; for example, when the influent ammonia nitrogen shows a continuous upward trend in the time series data, the system can predict the cumulative demand for oxygen for nitrification reaction in advance, providing a basis for subsequent dynamic oxygen demand assessment and time window division, avoiding the under-exposure or over-exposure problems caused by lag adjustment in traditional methods, and laying the foundation for real-time response and predictive adjustment for the entire dissolved oxygen control method.

[0055] In some embodiments, the input for dynamic oxygen demand assessment is the time-series water quality feature set generated in step S1, including historical and current data on chemical oxygen demand (COD), ammonia nitrogen content (NH3-N), mixed liquor suspended solids concentration (MLSS), and water temperature. By analyzing the time-series water quality feature set, a dynamic correlation model between changes in water quality parameters and dissolved oxygen demand is established, generating a dynamic dissolved oxygen range that reflects the oxygen demand intensity in the current and future time periods. The specific implementation process is as follows:

[0056] Step S21, calculate the instantaneous change rate of chemical oxygen demand and ammonia nitrogen content, for example, obtain the 5-minute change rate of COD through the sliding window difference method, and identify the sharp increase or slow decrease trend of organic matter load; perform a sliding window integral operation on the ammonia nitrogen concentration, for example, quantify its cumulative total amount in the past 1 hour, reflecting the growth rate of the potential oxygen demand of nitrification reaction; combine the suspended solids concentration of the mixed liquid and the water temperature to calculate the microbial metabolic rate index; for example, when the MLSS is 3000 mg / L and the temperature is 25°C, the metabolic rate baseline value is 1.0; for every 1°C increase in temperature, the index increases by 0.05 times.

[0057] Step S22: Calculate the oxygen demand for organic matter degradation based on the degradation kinetics model of chemical oxygen demand. The formula is as follows:

[0058] ThOD_COD=COD×(1-η)×α;

[0059] Wherein, ThOD_COD represents the amount of oxygen required to degrade organic matter in the influent; COD represents the measured concentration of chemical oxygen demand in the influent; η represents the COD removal efficiency, which is obtained by regression based on historical data; α is the oxygen conversion coefficient, which represents the amount of oxygen required to completely oxidize a unit mass of COD and is determined by the type of organic matter and the degree of oxidation; the COD value comes from the real-time monitoring data of the aeration tank inlet collected in step S1, rather than the COD value in the mixed liquor in the tank, and represents the current organic matter load entering the system. If step S1 uses time series data, the COD in the formula is usually the latest influent COD value to ensure real-time evaluation; (1-η) represents the proportion of COD that is not removed; for example, if the current COD removal efficiency η is 80% (η=0.8), the remaining 20% ​​of COD still requires oxygen consumption.

[0060] Step S23: Calculate the oxygen demand in the nitrification stage based on the accumulated ammonia nitrogen and the temperature correction factor. The formula is as follows:

[0061] ThOD_NH3=∫NH3-N×4.57×(1+0.03×(T-20));

[0062] Among them, ThOD_NH3 represents the oxygen demand in the nitrification stage;

[0063] ∫NH3-N is the integral of the influent ammonia nitrogen concentration within the time window, indicating the cumulative amount of ammonia nitrogen. The integral operation captures the continuous change trend of ammonia nitrogen concentration. For example, if the ammonia nitrogen concentration rises from 20 mg / L to 35 mg / L within 3 consecutive hours, the integral value reflects the cumulative effect of the oxygen demand for nitrification reaction.

[0064] 4.57 is the theoretical oxygen demand coefficient for complete nitrification of ammonia nitrogen; the stoichiometric formula of the nitrification reaction is:

[0065] NH3+2O2→NO3 - +H + +H2O; each oxidized 1mgNH3 - N needs to consume 4.57 mg of O2, which is calculated from the molar mass ratio: 2×32 g / mol / 14 g / mol≈4.57;

[0066] T represents the water temperature in the aeration tank. The temperature modifies the theoretical oxygen demand by affecting microbial activity and oxygen solubility;

[0067] 0.03 is the temperature correction factor, which indicates the adjustment ratio of oxygen demand for every 1°C deviation of the temperature from the base temperature (20°C). Based on the relationship between the metabolic rate of nitrifying bacteria and temperature in the activated sludge process, such as the Arrhenius equation, measured data show that the nitrification rate increases by about 3% for every 1°C increase in temperature.

[0068] Step S24: superimpose the oxygen demand for organic matter degradation and the oxygen demand for nitrification, and introduce the inhibitory factor of MLSS concentration on oxygen transfer efficiency to obtain the dynamic oxygen demand intensity. For example, when MLSS is greater than 3500 mg / L, the oxygen transfer efficiency decreases by 15%. The dynamic range is divided into the following three levels according to the total oxygen demand intensity:

[0069] Baseline requirements: oxygen demand ≤ 50kgO2 / h, corresponding to a DO range of 2.0-3.0mg / L;

[0070] Medium demand: 50-100 kg O2 / h, corresponding to a DO range of 3.0-4.0 mg / L;

[0071] Peak demand: >100kgO2 / h, corresponding to DO range 4.0-5.0mg / L.

[0072] In this embodiment, multi-dimensional oxygen demand analysis driven by time series data is used to avoid the limitations of fixed set values ​​or single-point feedback regulation in existing methods. Compared with adjustment based only on the current dissolved oxygen measurement value, this embodiment uses the dynamic evolution trend of historical and real-time water quality parameters to predict the changes in oxygen demand of microbial metabolic activities in advance. For example, when the water load continuously increases, the cumulative effect of nitrification oxygen demand changes the dissolved oxygen control from a lagging response to a forward-looking regulation. The generated dynamic dissolved oxygen range reflects the immediate demand of the current water quality for dissolved oxygen, realizes the dynamic matching of dissolved oxygen supply and biochemical reaction demand, and avoids underexposure, overexposure and energy waste caused by control lag.

[0073] In some embodiments, the time window division is based on the initial control instructions of the sewage treatment plant. The initial control instructions include information such as influent load forecast, effluent water quality standards, equipment operation plan, and energy consumption management targets. The specific division method is combined with the dynamic characteristics of the sewage treatment process and the control accuracy requirements, including:

[0074] Divide the control cycle into continuous windows at uniform time intervals, such as 15 minutes, 30 minutes, or 1 hour, to ensure the regularity and operability of the control strategy. For example, dividing a 24-hour day into 96 15-minute time windows allows the system to execute control at a fixed frequency, which is suitable for scenarios with relatively regular load fluctuations.

[0075] At the same time, by analyzing the fluctuation patterns of water inflow load in historical data, such as sudden load changes during peak hours in the morning and evening and industrial drainage periods, smaller windows can be set during periods of drastic load changes to improve control response speed; larger windows can be set during periods of stable load to reduce equipment wear and tear caused by frequent adjustments. For example, for the peak residential water consumption between 7:00 and 9:00 in the morning, the window can be refined to 15 minutes to match the rapid changes in short-term high loads.

[0076] In order to make the divided time windows more representative of the operating mode, for intermittent processes (such as SBR processes) or staged treatment processes (such as aerobic / anoxic / anaerobic sections of AAO processes), the windows are divided according to the time distribution of the process steps; for example, in the aerobic reaction stage of the SBR process, according to the time progress of the nitrification reaction, that is, the high-intensity aerobic stage in the first 2 hours and the maintenance stage in the last 1 hour, the stage is divided into multiple windows of different lengths, corresponding to different aerobic control targets.

[0077] Furthermore, the target dissolved oxygen range is determined primarily based on the initial sewage treatment control instructions. The target dissolved oxygen range is pre-set during the system design phase based on the characteristics of different treatment process stages. Specifically:

[0078] For the influent pretreatment stage, the main goal is to remove large suspended solids and some organic matter. The dissolved oxygen demand is relatively low, with a target range of 1.5-2.5 mg / L to maintain basic microbial activity.

[0079] For the biochemical reaction stage, differentiated targets are set according to different reaction processes. When the influent COD concentration is high, the target dissolved oxygen range is set at 2.0-3.5 mg / L to ensure sufficient degradation of organic matter; for NH3-N removal, the target dissolved oxygen range is increased to 3.0-4.5 mg / L to ensure the activity of nitrifying bacteria; in anoxic environments, the target dissolved oxygen is strictly controlled at 0.2-0.8 mg / L to promote denitrification.

[0080] For the sludge-water separation stage, a low DO level needs to be maintained to promote sludge sedimentation, with the target range set at 1.0-2.0 mg / L;

[0081] The corresponding target DO range is automatically matched according to the real-time monitored influent water quality and treatment stage, and a smooth transition between different stages is achieved through fuzzy logic algorithm.

[0082] On the other hand, aeration energy consumption constraints also originate from the initial control instructions and are formulated based on the overall energy efficiency goals of the sewage treatment plant and the equipment operating characteristics. Aeration energy consumption constraints include:

[0083] Fan frequency range: Based on the fan's physical characteristics and safe operation requirements, the minimum and maximum frequencies are set. The fan is a key device for providing air in the aeration system, and the fan frequency directly affects the aeration volume. The fan frequency range is specified. For example, the minimum frequency cannot be lower than a certain value to ensure basic aeration effect, and the maximum frequency cannot exceed the rated frequency of the equipment. At the same time, under different time windows or water quality conditions, the fan frequency range is dynamically adjusted according to energy consumption targets and dissolved oxygen requirements to ensure that the energy consumption of the fan operation is controlled within a reasonable range while meeting the sewage treatment needs.

[0084] Valve opening range: Set the minimum and maximum openings based on the aeration piping system design. The valve is used to regulate the air flow into the aeration tank, and the valve opening size determines the amount of air flowing through. Set the valve opening range, such as the minimum opening to ensure a certain amount of air entering, and the maximum opening not exceeding the valve's limit position. Based on a comprehensive consideration of the real-time dissolved oxygen gap and energy consumption constraints, adjust the valve opening under different operating conditions to ensure that the valve opening is within a range that meets both dissolved oxygen demand and energy consumption targets.

[0085] Energy consumption target per unit water volume: Based on the energy efficiency indicators of the sewage treatment plant, an upper limit for aeration energy consumption per ton of water is set, and energy consumption quotas are allocated according to time windows or process stages. Based on the overall energy consumption plan and actual operation of the sewage treatment plant, an energy consumption target per unit water volume is set. For example, the energy consumption per cubic meter of sewage treated cannot exceed a certain value. This target is determined and adjusted based on factors such as influent water quality, treatment process stage, and equipment operating efficiency. During actual operation, by adjusting parameters such as fan frequency and valve opening, as well as optimizing and controlling the entire aeration system, it is ensured that the actual energy consumption per unit water volume does not exceed the set energy consumption target, thereby achieving effective control of energy consumption.

[0086] In this embodiment, reasonable time window division, especially the setting of small windows during periods of drastic load changes, can capture changes in water quality and load more promptly, respond quickly, improve the accuracy of dissolved oxygen control, avoid lags in control instructions, and effectively solve the problem of control lagging behind actual water quality fluctuations in existing methods.

[0087] In some embodiments, the real-time dissolved oxygen gap is a quantitative indicator reflecting the difference between the current dynamic oxygen demand of water quality and the preset process target. The calculation objects are the dynamic dissolved oxygen range and the target dissolved oxygen range. The gap calculation needs to consider the difference between the upper and lower limits of the range at the same time to form a two-way quantitative result. The specific steps are as follows:

[0088] Extract the lower limit value (DO_dynamic_min) and upper limit value (DO_dynamic_max) of the dynamic dissolved oxygen range, as well as the lower limit value (DO_target_min) and upper limit value (DO_target_max) of the target dissolved oxygen range within the corresponding time window;

[0089] Calculate the lower limit gap and upper limit gap. The lower limit gap (ΔDO_min): reflects the difference between the actual lower limit of oxygen demand and the target lower limit. The calculation formula is:

[0090] ΔDO_min = DO_dynamic_min - DO_target_min. If the result is positive, it indicates that the dynamic lower limit of demand is higher than the target lower limit; if it is negative, it means that the dynamic lower limit of demand is lower than the target lower limit. The upper limit gap (ΔDO_max) reflects the difference between the actual upper limit of oxygen demand and the target upper limit. The calculation formula is: ΔDO_max = DO_dynamic_max - DO_target_max. Similarly, a positive value indicates that the dynamic upper limit is higher than the target upper limit, and a negative value indicates that the dynamic upper limit is lower than the target upper limit;

[0091] Combining the signs and absolute value magnitudes of the lower limit and upper limit gaps, judge the current dissolved oxygen supply - demand state. When ΔDO_min ≥ 0 and ΔDO_max ≥ 0, the dynamic range is completely higher than the target range, there is an oxygen surplus gap, which may lead to over - aeration; when ΔDO_min ≤ 0 and ΔDO_max ≤ 0, the dynamic range is completely lower than the target range, there is an oxygen deficiency gap, which may lead to under - aeration; when the dynamic range and the target range partially overlap, such as DO_dynamic_min < DO_target_min but DO_dynamic_max > DO_target_min, evaluate the actual supply - demand matching degree through the proportion of the overlapping interval. The gap is mainly based on the boundary difference of the uncovered part.

[0092] It should be noted that the dynamic dissolved oxygen range is based on the high - frequency time - series data of step S1, and the target dissolved oxygen range changes with the switching of the time window, ensuring that the gap calculation synchronously reflects the difference between the latest oxygen demand state and the control target of the current stage, and avoiding the lag problem of a single fixed value in traditional methods; the lower limit gap and the upper limit gap respectively correspond to the minimum and maximum adjustment demands of the aeration system. For example, when ΔDO_min is - 1.0 mg / L, that is, the dynamic lower limit is 1.0 mg / L lower than the target lower limit, it is necessary to increase the aeration volume to increase the dissolved oxygen concentration; when ΔDO_max is + 0.5 mg / L, that is, the dynamic upper limit is 0.5 mg / L higher than the target upper limit, it is necessary to reduce the aeration volume to avoid excessive consumption;

[0093] Through the above calculation logic, the abstract supply and demand difference is converted into a quantifiable control signal, providing a direct basis for the precise adjustment of the subsequent aeration system. This solves the problems of empirical adjustment or single-point feedback lag in existing methods, achieves dynamic matching of dissolved oxygen supply and biochemical reaction requirements, and takes into account both treatment efficiency and energy consumption optimization.

[0094] In some embodiments, the inputs for the coupled analysis of the real-time dissolved oxygen gap and the aeration energy consumption constraint are the real-time dissolved oxygen gap obtained in step S4 and the aeration energy consumption constraint determined in step S3. Under the premise of satisfying the energy consumption constraint, the operating parameters of the aeration equipment are adjusted to match the dissolved oxygen concentration with the biochemical reaction requirements. The aeration control parameters include fan frequency, valve opening, and sludge return ratio. The specific coupled analysis is as follows:

[0095] The urgency of the current supply and demand situation is determined based on the sign and absolute value of the difference between the lower and upper limits of the real-time dissolved oxygen gap. When the lower limit gap ΔDO_min is negative and its absolute value exceeds the preset threshold, it indicates that the dynamic demand lower limit is significantly lower than the target lower limit, and the risk of insufficient oxygen demand is high, triggering a priority oxygen replenishment instruction. When the upper limit gap ΔDO_max is positive and the dynamic range is completely higher than the target range, it is determined to be an oxygen surplus, triggering a frequency reduction and throttling instruction. If the gap is in an overlapping range, the adjustment weight is allocated based on the difference ratio of the uncovered boundary to ensure that the aeration volume accurately matches the gap shape.

[0096] The aeration energy consumption constraint parameters within the current time window, including the allowable range of fan frequency, valve opening limit, and energy consumption quota per unit water volume, are used as boundary conditions for the regulation operation. For scenarios requiring oxygen supplementation, the theoretical fan frequency increase or valve opening expansion corresponding to the required aeration increment is calculated and compared with the maximum allowable value in the energy consumption constraint. If the theoretical value exceeds the upper limit, the constraint upper limit is used as the regulation benchmark, and the multi-device collaborative increment is allocated through an optimization algorithm. For scenarios requiring energy reduction, the regulation range is dynamically adjusted based on the remaining energy consumption quota. If the energy consumption per unit water volume in the current window is close to the upper limit, valve opening reduction is preferred over fan frequency reduction to maintain fan operation stability.

[0097] An optimization model is constructed with the minimization of dissolved oxygen gap and the minimization of energy consumption as the objective functions. The input variables include fan frequency, valve opening and sludge return ratio, and the constraints include the physical limits of the equipment, window energy consumption quota and biochemical reaction requirements. The linear weighting method or Pareto front analysis is used to dynamically allocate the priority weights of treatment effect and energy consumption. For example, when the effluent water quality exceeds the critical limit, the closure of the dissolved oxygen gap is given priority. When the energy consumption exceeds the limit warning, the rigid execution of the constraints is emphasized. Combined with the real-time system status, the preset rule library is called to make quick decisions. For example, when ΔDO_min is -0.8 mg / L and the remaining energy consumption quota is sufficient, the maximum allowable frequency is increased by 20% and the valve is opened to the middle opening at the same time. If the quota is insufficient, the gradient adjustment mode is started to approach the gap closure in steps.

[0098] Furthermore, to avoid frequent equipment start-up and shutdown or sudden parameter changes, the optimization results are smoothed and historical status memory is stored. Based on the fan frequency and valve opening of the previous control cycle, an upper limit for the single adjustment amplitude is set, such as the fan frequency change does not exceed ±5Hz / minute, and the valve opening adjustment step is ≤10%. At the same time, a feedforward compensation mechanism is introduced to predict the gap trend of the next cycle based on the time-series water quality feature set, and pre-adjust the aeration intensity. For example, if the ammonia nitrogen integral trend shows that the oxygen demand will continue to rise, a 5% aeration margin is reserved in the current adjustment.

[0099] The sludge return ratio is adjusted in conjunction with the deviation between the real-time value of the mixed liquor suspended solids concentration MLSS and the target range; when the MLSS is lower than the threshold and the dissolved oxygen gap is negative, the return ratio is appropriately increased to increase the activated sludge concentration, indirectly improving the oxygen utilization efficiency and reducing the demand for pure aeration oxygen supplementation; if the MLSS is too high, resulting in a decrease in oxygen transfer efficiency, the return ratio is reduced and the oxygen demand gap is compensated by the aeration system first, so as to avoid excessive sludge concentration exacerbating energy consumption losses.

[0100] In this embodiment, multi-objective collaborative optimization is achieved by dynamically evaluating the matching relationship between the dissolved oxygen gap and the energy consumption constraint. It has the advantages of dynamic response capability and multi-parameter collaboration, can accurately identify the risk of insufficient or excessive oxygen demand and trigger hierarchical instructions, and constructs constraint boundaries in combination with the physical limits of the aeration equipment and the energy consumption quota to avoid equipment loss and energy consumption surge caused by over-limit regulation. Linear weighting and Pareto front analysis are used to balance the treatment effect and energy consumption cost, and minimize aeration energy consumption while ensuring the effluent water quality. The feedforward compensation mechanism and historical state memory are used to suppress parameter mutations. Combined with MLSS linkage regulation, the oxygen mass transfer efficiency is improved, the dependence on pure aeration is reduced, and the system's adaptability to load fluctuations is enhanced. Its hierarchical decision-making and gradient regulation mode effectively balance the regulation accuracy and equipment life.

[0101] like Figure 2 As shown, the dissolved oxygen control system applied to the aeration system of the present invention specifically includes the following modules:

[0102] A data processing module is used to collect water quality parameter information of the aeration tank inlet and the tank based on a preset data acquisition frequency, and to arrange at least two sets of water quality parameter information in time series to generate a time series water quality feature set;

[0103] A dynamic oxygen demand assessment module is used to perform dynamic oxygen demand assessment on a time series water quality feature set to obtain a dynamic dissolved oxygen range; the dynamic dissolved oxygen range can accurately represent the intensity of the current water quality's demand for dissolved oxygen;

[0104] The target parameter determination module is used to divide multiple time windows according to the initial sewage treatment control instructions, and determine the corresponding target dissolved oxygen range and aeration energy consumption constraints for each time window, so as to formulate clear goals and constraints for subsequent control strategies;

[0105] The gap calculation module is used to calculate the real-time dissolved oxygen gap between the dynamic dissolved oxygen range and the target dissolved oxygen range within the corresponding time window, quantifying the difference between the current actual demand and the target;

[0106] The control module is used to couple the dissolved oxygen gap with the aeration energy consumption constraint to obtain real-time aeration control parameters, which are then used to adjust the aeration system to achieve precise control of the aeration system and meet the dynamic demand for dissolved oxygen during sewage treatment.

[0107] In this embodiment, the refined dynamic control of the aeration process is achieved through the collaboration of multiple modules, and the time series water quality feature analysis is deeply integrated with the multi-time window target management to solve the control lag problem caused by the traditional system's reliance on fixed set values ​​or instantaneous feedback; the data processing module captures the continuous change trend of the influent load through high-frequency, multi-parameter time series integration, providing a data basis for dynamic oxygen demand assessment; the dynamic oxygen demand assessment module outputs the dynamic dissolved oxygen range based on the time series characteristics, and predicts the demand trend of DO for biochemical processes such as nitrification in advance, breaking through the limitation of relying solely on the current DO measurement value; The target parameter determination module decomposes the long-term control task into short-term sub-targets, and sets differentiated DO targets and energy consumption constraints in combination with the sewage treatment plant operation plan to achieve a dynamic balance between treatment demand and economy; the gap calculation module quantifies the deviation between real-time demand and target, accurately identifies the risk of underexposure or overexposure, and provides a decision-making basis for the control module; the control module adopts a multi-objective optimization algorithm to meet the real-time gap compensation while taking into account energy consumption constraints. By adjusting multiple parameters such as fan frequency and valve opening, it avoids ultra-high energy consumption compensation in the lag stage and prevents energy waste caused by excessive aeration.

[0108] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A dissolved oxygen control method for an aeration system, characterized in that: The method comprises: Based on the preset data acquisition frequency, the water quality parameter information of the aeration tank inlet and the tank is obtained, and at least two sets of water quality parameter information are arranged in time sequence to obtain a time series water quality feature set; Performing a dynamic oxygen demand assessment on the time series water quality feature set to obtain a dynamic dissolved oxygen range; including: Calculate the instantaneous rate of change of chemical oxygen demand and ammonia nitrogen content to identify the sharp increase or slow decrease trend of organic matter load; perform a sliding window integral operation on ammonia nitrogen concentration to reflect the growth rate of potential oxygen demand for nitrification reaction; combine the suspended solids concentration of the mixed solution with the water temperature to calculate the microbial metabolic rate index; Calculate the oxygen demand for organic matter degradation based on the degradation kinetics model of chemical oxygen demand; Calculate the oxygen demand during the nitrification stage based on the accumulated ammonia nitrogen and the temperature correction factor; The oxygen demand for organic matter degradation and the oxygen demand for nitrification are superimposed, and the inhibition factor of the mixed liquid suspended solids concentration on oxygen transfer efficiency is introduced to obtain the dynamic oxygen demand intensity. Based on the initial sewage treatment control instructions, multiple time windows are divided, and the target dissolved oxygen range and aeration energy consumption constraints within each time window are determined; the target dissolved oxygen range switches with the time window; Calculating a real-time dissolved oxygen gap ΔDO between the dynamic dissolved oxygen range and the target dissolved oxygen range within a corresponding time window includes: Extract the lower limit of the dynamic dissolved oxygen range DO dynamic,min With the upper limit DO dynamic,max , and the lower limit of the target dissolved oxygen range DO in the corresponding time window target,min With the upper limit DO target,max ; Calculate the lower limit gap ΔDO min and upper limit gap ΔDO max , ΔDO min =DO dynamic,min -DO target,min ;ΔDO max =DO dynamic,max -DO target,max ; The current dissolved oxygen supply and demand status is judged by combining the signs and absolute values ​​of the lower and upper limit gaps. When the dynamic dissolved oxygen range is completely higher than the target range, there is an oxygen surplus gap; when the dynamic dissolved oxygen range is completely lower than the target range, there is an oxygen shortage gap; when the dynamic dissolved oxygen range partially overlaps with the target range, the actual supply and demand matching degree is evaluated by the proportion of the overlapping interval. Coupling analysis of the real-time dissolved oxygen gap and the aeration energy consumption constraint to obtain real-time aeration control parameters, and adjusting the aeration system accordingly; The water quality parameter information includes chemical oxygen demand, ammonia nitrogen content, mixed liquid suspended matter concentration and water temperature; An optimization model with the objective function of minimizing the dissolved oxygen gap and minimizing energy consumption is constructed. The input variables include fan frequency, valve opening and sludge return ratio. The constraints include the physical limit of the equipment, window energy consumption quota and biochemical reaction requirements. The linear weighting method or Pareto front analysis is used to dynamically allocate the priority weights of treatment effect and energy consumption. When the effluent water quality exceeds the critical limit, the closure of the dissolved oxygen gap is given priority. When the energy consumption exceeds the limit warning is issued, the rigid execution of the constraints is emphasized. Combined with the real-time system status, the preset rule library is called for rapid decision-making. When ΔDO min When the concentration is -0.8mg / L and the remaining energy consumption quota is sufficient, the maximum allowable frequency is increased by 20% and the valve is opened to the middle opening simultaneously; if the quota is insufficient, the gradient adjustment mode is activated to close the gap step by step; The optimization results are smoothed and historical states are memorized. Based on the fan frequency and valve opening of the previous control cycle, the upper limit of the single adjustment amplitude is set, and a feedforward compensation mechanism is introduced. The gap trend of the next cycle is predicted based on the time-series water quality feature set, and the aeration intensity is pre-adjusted.

2. The dissolved oxygen control method for an aeration system according to claim 1, wherein: The aeration control parameters include fan frequency, valve opening and sludge return ratio.

3. The dissolved oxygen control method for an aeration system according to claim 1, wherein: The calculation formula for the oxygen demand for organic matter degradation is: THOD COD =COD×(1-η)×α; Among them, ThOD COD It represents the amount of oxygen required to degrade organic matter in the influent; COD represents the measured concentration of chemical oxygen demand in the influent; η represents the COD removal efficiency; α is the oxygen conversion coefficient, which represents the amount of oxygen required for complete oxidation of unit mass of COD.

4. The dissolved oxygen control method for an aeration system according to claim 3, wherein: The calculation formula for the oxygen demand in the nitrification stage is: ThOD NH3 =∫NH3-N×4.57×(1+0.03×(T-20)); Among them, ThOD NH3 represents the oxygen demand in the nitrification stage; ∫NH3-N is the integral of the influent ammonia nitrogen concentration within the time window, indicating the cumulative amount of ammonia nitrogen; 4.57 is the theoretical oxygen demand coefficient for complete nitrification of ammonia nitrogen; T represents the water temperature in the aeration tank; 0.03 is the temperature correction factor, indicating the adjustment ratio of oxygen demand for every 1°C deviation of temperature from the base temperature.

5. The dissolved oxygen control method for an aeration system according to any one of claims 1 to 4, characterized in that: The aeration energy consumption constraints include fan frequency range, valve opening range and unit water energy consumption target.

6. A dissolved oxygen control system applied to an aeration system, characterized in that: The system is applied to the dissolved oxygen control method for an aeration system according to claim 1, and the system comprises: A data processing module is used to collect water quality parameter information of the aeration tank inlet and the tank based on a preset data acquisition frequency, and to arrange at least two sets of water quality parameter information in time series to generate a time series water quality feature set; Dynamic oxygen demand assessment module, used to perform dynamic oxygen demand assessment on the time series water quality feature set to obtain the dynamic dissolved oxygen range; The target parameter determination module is used to divide multiple time windows according to the initial sewage treatment control instructions, and determine the corresponding target dissolved oxygen range and aeration energy consumption constraint for each time window; Gap calculation module, used to calculate the real-time dissolved oxygen gap between the dynamic dissolved oxygen range and the target dissolved oxygen range in the corresponding time window; The control module is used to couple the dissolved oxygen real-time gap and aeration energy consumption constraints to obtain real-time aeration control parameters and adjust the aeration system accordingly.

7. An electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, wherein: When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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