Dissolved oxygen control method and system applied to aeration system

Abstract

The invention relates to the technical field of sewage treatment, in particular to a dissolved oxygen control method and system applied to an aeration system, and the method comprises the following steps: based on a preset data acquisition frequency, obtaining water quality parameter information of inlet water and the inside of an aeration tank, and arranging at least two groups of water quality parameter information according to a time sequence to obtain a time sequence water quality characteristic set; performing dynamic aerobic evaluation on the time sequence water quality characteristic set to obtain a dynamic dissolved oxygen range; dividing a plurality of time windows according to the initial sewage treatment regulation and control instruction, and determining a target dissolved oxygen range and an aeration energy consumption constraint in each time window; calculating a dissolved oxygen real-time gap between the dynamic dissolved oxygen range and a target dissolved oxygen range in the corresponding time window; carrying out constraint coupling analysis on the dissolved oxygen real-time notch and the aeration energy consumption to obtain real-time aeration control parameters, and adjusting an aeration system according to the real-time aeration control parameters; the problem of underexposure or overexposure caused by lagging adjustment can be avoided, and meanwhile energy waste caused by frequent overshoot is reduced.

Description

Technical Field

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

[0002] In the activated sludge process for sewage treatment, the aeration system is a key link to maintain the normal progress of the biochemical reaction. Its main function is to provide oxygen required for microorganisms to decompose organic matter in the aeration tank, and at the same time play a role in stirring and mixing to ensure full contact between microorganisms and sewage. The precise control of dissolved oxygen (DO) is directly related to the efficiency, energy consumption, and effluent quality of sewage treatment.

[0003] Existing dissolved oxygen control methods usually adopt a fixed dissolved oxygen set value or feedback regulation based on instantaneous water quality parameters, resulting in a lag of control instructions 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 increase, the oxygen demand for nitrification accumulates and increases accordingly. However, the existing methods only rely on the dissolved oxygen measurement value at the current moment for adjustment and cannot predict the oxygen demand trend in subsequent 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 volume with ultra-high energy consumption during the lag phase, causing energy waste or a decline in treatment effect. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a dissolved oxygen control method and system applied to an aeration system, which can avoid the problems of under-aeration or over-aeration caused by lag adjustment, and at the same time reduce the energy waste caused by frequent overshoot.

[0005] In the first aspect, the present invention provides a dissolved oxygen control method applied to an aeration system, including: Based on a preset data collection frequency, obtain the water quality parameter information of the influent and inside the aeration tank, and arrange at least two groups of water quality parameter information in time sequence to obtain a time-sequence water quality feature set; Conduct a dynamic oxygen demand assessment on the time-sequence water quality feature set to obtain a dynamic dissolved oxygen range; According to the initial sewage treatment regulation instruction, divide multiple time windows, and determine the target dissolved oxygen range and aeration energy consumption constraint within each time window; Calculate the real-time dissolved oxygen gap between the dynamic dissolved oxygen range and the target dissolved oxygen range within the corresponding time window; Conduct a coupled analysis of the real-time dissolved oxygen gap and the aeration energy consumption constraint to obtain real-time aeration control parameters, and adjust the aeration system accordingly.

[0006] Further, the water quality parameter information includes chemical oxygen demand, ammonia nitrogen content, mixed liquor suspended solids concentration, and water body temperature.

[0007] Further, the aeration control parameters include the fan frequency, valve opening degree, and sludge return ratio.

[0008] Further, a dynamic aerobic demand assessment is performed on the time-series water quality characteristic set, including: Calculating the oxygen demand for organic matter degradation based on the degradation kinetic model of chemical oxygen demand; Calculating the oxygen demand in the nitrification stage according to the ammonia nitrogen accumulation amount and temperature correction factor; Superposing the oxygen demand for organic matter degradation and the oxygen demand in the nitrification stage, and introducing the inhibition factor of the mixed liquor suspended solids concentration on the oxygen transfer efficiency to obtain the dynamic aerobic intensity.

[0009] Further, the calculation formula for the oxygen demand for organic matter degradation is: ThOD_COD = COD×(1 - η)×α; Wherein, ThOD_COD represents the amount of oxygen required to degrade organic matter in the influent; COD represents the measured concentration value of chemical oxygen demand in the influent; η represents the COD removal efficiency; α is the oxygen conversion coefficient, representing the amount of oxygen required for complete oxidation of unit mass of COD.

[0010] Further, the calculation formula for the oxygen demand in the nitrification stage is: ThOD_NH3 = ∫NH3-N×4.57×(1 + 0.03×(T - 20)); Wherein, 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, representing the ammonia nitrogen accumulation amount; 4.57 is the theoretical oxygen demand coefficient for complete nitrification of ammonia nitrogen; T represents the water body temperature in the aeration tank; 0.03 is the temperature correction factor, representing the adjustment ratio of the oxygen demand when the temperature deviates from the reference temperature by 1°C.

[0011] Further, the aeration energy consumption constraint includes the fan frequency range, valve opening degree range, and the target energy consumption per unit water volume.

[0012] On the other hand, the present application also provides a dissolved oxygen control system applied to an aeration system, and the system includes: A data processing module, configured to collect the water quality parameter information of the influent and in the aeration tank based on a preset data collection frequency, and arrange at least two groups of water quality parameter information in time series to generate a time-series water quality characteristic set; A dynamic aerobic demand assessment module, configured to perform a dynamic aerobic demand assessment on the time-series water quality characteristic set to obtain a dynamic dissolved oxygen range; A target parameter determination module, configured to divide a plurality of time windows according to an initial sewage treatment control instruction, and determine a corresponding target dissolved oxygen range and aeration energy consumption constraint for each time window; A notch calculation module for calculating the real-time dissolved oxygen notch between the dynamic dissolved oxygen range and the target dissolved oxygen range within the corresponding time window; A control module for performing a coupling analysis on the real-time dissolved oxygen notch and the aeration energy consumption constraint to obtain real-time aeration control parameters and adjusting the aeration system accordingly.

[0013] In a third aspect, the present application provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are connected through the bus. When the computer program is executed by the processor, the steps in the method described in any one of the above are implemented.

[0014] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the method described in any one of the above are implemented.

[0015] The beneficial effects of the present invention compared with the prior art are as follows: Existing methods rely on fixed dissolved oxygen set values or single-point data at the current moment for adjustment and cannot capture the temporal variation trend of water quality parameters. This method constructs a set of temporal water quality characteristics, incorporates at least two sets of historical data into the analysis, can identify the dynamic evolution law of water quality parameters, upgrades the dissolved oxygen control from immediate response to trend prediction, anticipates the direction of oxygen demand change in advance, and avoids under-aeration or over-aeration problems caused by lagged adjustment. The dissolved oxygen range generated through dynamic oxygen demand assessment, rather than a fixed value, can match the change in oxygen demand intensity under water quality fluctuations in real time, avoids the extensive adjustment mode of non-compliance compensation only under a fixed threshold, enables the aeration system to maintain a smooth response during the gradual change of water quality, and reduces energy consumption waste caused by frequent overshoots. Based on the initial control instruction, divide the time window and set independent target dissolved oxygen ranges and energy consumption constraints for each window, breaking down the long-term goal of sewage treatment into short-term controllable sub-goals. For example, within the time window of the peak influent load period, a slightly higher dissolved oxygen target is allowed to ensure the treatment effect, while over-aeration is avoided through energy consumption constraints. Reduce the dissolved oxygen target and strictly limit the energy consumption during low load periods to achieve precise regulation by time period and avoid the neglect of the balance between energy consumption and effect in the one-size-fits-all adjustment of existing methods. By calculating the real-time notch between the dynamic dissolved oxygen range and the target dissolved oxygen range and performing a coupling analysis in combination with the energy consumption constraint, the generation of aeration control parameters simultaneously meets the dual goals of oxygen demand compensation and optimal energy consumption, minimizes energy consumption on the premise of ensuring the treatment effect, and avoids the single-goal adjustment defect of disregarding costs for compliance in existing methods. Description of the Drawings

[0016] Figure 1 It is a flowchart of the dissolved oxygen control method applied to the aeration system in the present invention; Figure 2 It is a structural diagram of the dissolved oxygen control system applied to the aeration system in the present invention. Detailed implementation manners

[0017] The present application will be described below in conjunction with the accompanying drawings in the present application.

[0018] As Figure 1 shown, the dissolved oxygen control method applied to the aeration system of the present invention specifically includes the following steps: Step S1: Based on a preset data acquisition frequency, obtain the water quality parameter information of the influent and inside the aeration tank, and arrange at least two groups of water quality parameter information in time sequence to obtain a time-sequence water quality feature set; Step S2: Conduct a dynamic aerobic assessment on the time-sequence water quality feature set to obtain a dynamic dissolved oxygen range; Step S3: According to the initial sewage treatment regulation instruction, divide multiple time windows, and determine the target dissolved oxygen range and aeration energy consumption constraint within each time window; Step S4: Calculate the real-time dissolved oxygen gap between the dynamic dissolved oxygen range and the target dissolved oxygen range within the corresponding time window; Step S5: Conduct a coupled analysis on the real-time dissolved oxygen gap and the aeration energy consumption constraint to obtain real-time aeration control parameters, and adjust the aeration system accordingly.

[0019] In this embodiment, through the construction of the time-sequence water quality feature set, at least two groups of historical data are incorporated into the analysis, which can identify the dynamic evolution law of water quality parameters, upgrade the dissolved oxygen control from immediate response to trend prediction, perceive the direction of aerobic change in advance, and avoid under-aeration or over-aeration problems caused by lag adjustment; the dissolved oxygen range generated through dynamic aerobic assessment, rather than a fixed value, can match the change in aerobic intensity under water quality fluctuations in real time, avoid the rough adjustment mode of non-compliance compensation under a fixed threshold, make the aeration system maintain a smooth response during the gradual change of water quality, and reduce energy consumption waste caused by frequent overshoot; Divide time windows based on the initial regulation instruction, and set independent target dissolved oxygen ranges and energy consumption constraints for each window, breaking down the long-term goal of sewage treatment into short-term controllable sub-goals; for example, within the time window of the peak influent load period, a slightly higher dissolved oxygen target is allowed to ensure the treatment effect, and at the same time, over-aeration is avoided through the energy consumption constraint; reduce the dissolved oxygen target and strictly limit the energy consumption during the low-load period to achieve precise regulation by time period, and avoid the neglect of the balance between energy consumption and effect in the one-size-fits-all adjustment method in the existing methods; By calculating the real-time gap between the dynamic dissolved oxygen range and the target dissolved oxygen range, and conducting coupled analysis in combination with energy consumption constraints, the generation of aeration control parameters can simultaneously meet the dual objectives of aerobic compensation and optimal energy consumption, minimize energy consumption on the premise of ensuring treatment effect, and avoid the single-objective regulation defect of ignoring costs for compliance in existing methods; In terms of the overall logic, this method constructs an active control system of "data-driven + goal-oriented" through the logical chain of "time-series data modeling → dynamic demand prediction → time window planning → coupled optimization decision-making"; for example, the time-series water quality feature set provides a trend basis for dynamic aerobic assessment, the time window transforms the long-term regulation goal into an executable short-term strategy, and the coupled analysis of the dissolved oxygen gap and energy consumption constraints ensures that each short-term strategy is implemented under the balance of "effect - energy consumption", forming a closed-loop optimization from data perception to execution adjustment, significantly improving the system's adaptability to continuous load changes, such as avoiding the problem of long-term under-aeration caused by the continuous increase in influent ammonia nitrogen; Through multi-dimensional data fusion and coupling of constraint conditions, the collaborative optimization of energy consumption and treatment effect is realized. The dynamic aerobic assessment determines the DO range based on the actual water quality requirements, avoiding ineffective aeration caused by excessive safety margins, such as setting too high a fixed DO value in traditional methods to cope with load fluctuations; the time window division enables the system to allocate resources at different times, such as actively reducing the DO target and restricting energy consumption during low-load periods at night, while the calculation of the dissolved oxygen gap ensures that the adjustment amount within each window precisely matches the actual demand, avoiding a sharp increase in energy consumption caused by over-compensation, such as having to use an extremely high aeration volume to make up for the under-oxygen state due to lagged regulation in existing methods; In summary, this embodiment forms an intelligent control system that adapts to dynamic water quality changes through the three-layer logic collaboration of time-series feature-driven demand prediction, time window decomposition of regulation goals, and coupled analysis to balance multiple constraints. While avoiding the lag and extensiveness defects of traditional methods, it realizes the in-depth optimization of sewage treatment efficiency and energy consumption, and is especially suitable for complex working conditions with frequent fluctuations in influent load.

[0020] In some embodiments, the data acquisition scope covers the key water quality parameters at the inlet end of the aeration tank and the mixed liquor in the tank; the parameters at the inlet end include chemical oxygen demand (COD) and ammonia nitrogen content (NH3-N), and both 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 a large amount of dissolved oxygen is consumed during the process of converting ammonia nitrogen into nitrate; the parameters in the tank include the mixed liquor suspended solids concentration (MLSS) and the water body temperature. Among them, MLSS reflects the total amount of microorganisms in the activated sludge, and an increase in its value means that more microorganisms participate in the biochemical reaction, and the overall oxygen demand increases synchronously with the metabolic activity; the water body temperature affects the activity of microbial enzymes and the solubility of oxygen in water, and temperature changes will directly change the biochemical reaction rate and the dissolved oxygen demand. For example, for every 10°C increase in temperature, the microbial metabolic rate increases by about 1 time, and the oxygen demand increases accordingly.

[0021] At the same time, the preset data collection frequency is set to the minute level, such as once every 5 to 15 minutes, to match the dynamic characteristics of the sewage treatment process; the inlet load may change significantly in a short time due to factors such as industrial wastewater discharge and water use peaks. High-frequency collection 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 reaction in the tank is relatively slow, the cumulative effect of microbial metabolism needs to identify trends through continuous data monitoring. For example, a 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 information; at the same time, the sensors need to be calibrated regularly to avoid drift errors affecting subsequent analysis and ensure the reliability of the data.

[0022] Furthermore, at least two groups are arranged in chronological order to form a dynamic data set including historical and current states; for example, collecting data for the past 2 hours at 10-minute intervals can generate 12 groups of time-series data, each group covering four characteristics: COD, NH3-N, MLSS, and temperature. The chronological arrangement not only records the current water quality state, but also reflects the parameter change trend through the data sequence, such as the ammonia nitrogen concentration rising for 3 consecutive cycles and the COD fluctuation range gradually narrowing; the trend information can identify the directionality (such as increasing or decreasing) and rate (change slope) of the load change, providing input for subsequent dynamic oxygen demand assessment; for example, the inlet COD continuously rises from 300 mg / L to 500 mg / L, indicating a sudden increase in the organic matter load and the upcoming oxygen demand peak; the MLSS in the tank is stable at about 3000 mg / L, indicating that the microbial concentration is in a steady state, and the oxygen demand fluctuation is mainly driven by the inlet substrate load.

[0023] In this embodiment, through multi-dimensional parameter collection and time-series processing, a basic data set reflecting the dynamic changes in water quality is constructed, effectively solving the lag problem of relying only on single-point data at the current moment in the existing methods; the time-series feature set contains the historical evolution information of the parameters, enabling the system to capture the continuous change trends of the influent load and the in-tank conditions, rather than isolated instantaneous states; for example, when the influent ammonia nitrogen shows a continuous upward trend in the time-series data, the system can predict in advance the cumulative demand for oxygen in the nitrification reaction, providing a basis for subsequent dynamic oxygen demand assessment and time window division, avoiding the problems of under-aeration or over-aeration caused by lag adjustment in the traditional methods, and laying a foundation for real-time response and predictive regulation for the entire dissolved oxygen control method.

[0024] In some embodiments, the input for dynamic oxygen demand assessment is the time-series water quality feature set generated in step S1, which includes the historical and current data of chemical oxygen demand (COD), ammonia nitrogen content (NH3-N), mixed liquor suspended solids concentration (MLSS), and water body temperature; by analyzing the time-series water quality feature set, a dynamic correlation model between the changes in water quality parameters and the oxygen demand is established, generating a dynamic dissolved oxygen range reflecting the oxygen demand intensity in the current and future periods; the specific implementation process is as follows: Step S21: Calculate the instantaneous change rates of chemical oxygen demand and ammonia nitrogen content. For example, obtain the 5-minute change rate of COD through the sliding window difference method to identify the steep increase or slow decrease trend of the organic matter load; perform a sliding window integration operation on the ammonia nitrogen concentration, for example, quantify its cumulative total in the past 1 hour to reflect the growth amplitude of the potential oxygen demand in the nitrification reaction; combine the mixed liquor suspended solids concentration and the water body temperature to calculate the microbial metabolism rate index; for example, when the MLSS is 3000 mg / L and the temperature is 25°C, the metabolism rate reference value is 1.0; for each 1°C increase in temperature, the index increases by 0.05 times.

[0025] Step S22: Based on the degradation kinetic model of chemical oxygen demand, calculate the oxygen demand for organic matter degradation. The formula is as follows: ThOD_COD = COD × (1 - η) × α; Among them, ThOD_COD represents the amount of oxygen required to degrade the organic matter in the influent; COD represents the measured concentration value of the 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, representing the amount of oxygen required for the complete oxidation of unit mass of COD, which is determined by the type and degree of oxidation of the organic matter; the COD value comes from the real-time monitoring data of the inlet of the aeration tank collected in step S1, rather than the COD value in the mixed liquor in the tank, representing the organic matter load currently entering the system. If step S1 uses time-series data, the COD in the formula usually takes the inlet COD value at the latest moment to ensure the real-time nature of the evaluation; (1 - η) represents the proportion of COD that has not been removed. For example, if the current COD removal efficiency η is 80% (η = 0.8), then the remaining 20% of the COD still needs to consume oxygen.

[0026] Step S23: Calculate the oxygen demand in the nitrification stage according to the ammonia nitrogen accumulation amount and the temperature correction factor. The formula is as follows: 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, representing the accumulation amount of ammonia nitrogen. The integral operation captures the continuous change trend of the 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 the nitrification reaction; 4.57 is the theoretical oxygen demand coefficient for the complete nitrification of ammonia nitrogen. The stoichiometric formula for the nitrification reaction is: NH3 + 2O2 → NO3 - + H + + H2O; For every 1 mg of NH3 - N oxidized, 4.57 mg of O2 needs to be consumed, which is calculated from the molar mass ratio: 2 × 32 g / mol / 14 g / mol ≈ 4.57; T represents the water temperature in the aeration tank. The temperature modifies the theoretical oxygen demand by affecting the microbial activity and oxygen solubility; 0.03 is the temperature correction factor, representing the adjustment ratio of the oxygen demand for every 1 °C deviation of the temperature from the reference 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, the measured data show that for every 1 °C increase in temperature, the nitrification rate increases by about 3%.

[0027] Step S24: Superimpose the oxygen demand for organic matter degradation and the oxygen demand in the nitrification stage, and introduce the inhibition factor of the MLSS concentration on the oxygen transfer efficiency to obtain the dynamic oxygen demand intensity. For example, when MLSS > 3500 mg / L, the oxygen transfer efficiency decreases by 15%. Divide the dynamic range into the following three levels according to the total oxygen demand intensity: Basic requirement: oxygen demand ≤50kgO2 / h, corresponding to DO range 2.0-3.0mg / L; Medium demand: 50-100kgO2 / h, corresponding to DO range 3.0-4.0mg / L; Peak demand: >100kgO2 / h, corresponding to DO range 4.0-5.0mg / L.

[0028] In this embodiment, the limitations of fixed set values ​​or single-point feedback regulation in existing methods are avoided through multi-dimensional oxygen demand analysis driven by time series data. 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 inlet load continues to increase, 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.

[0029] In some embodiments, the time window division is based on the initial control instructions of the sewage treatment plant. The initial control instructions cover information such as inlet load forecast, effluent water quality standards, equipment operation plan and energy consumption management goals. The specific division method is combined with the dynamic characteristics of the sewage treatment process and the control accuracy requirements, including: 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, divide the 24 hours of the day into 96 15-minute time windows, so that the system can perform control at a fixed frequency, which is suitable for scenarios with regular load fluctuations; At the same time, by analyzing the fluctuation pattern of water inflow load in historical data, such as load mutations 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 loss caused by frequent adjustments; for example, for the peak of residential water use between 7:00 and 9:00 in the morning, the window can be refined to 15 minutes to match the rapid changes of short-term high loads; In order to make the divided time windows more representative of the operating mode, for intermittent processes (such as SBR processes) or segmented treatment processes (such as aerobic / anoxic / anaerobic sections of AAO processes), the windows are divided according to the time distribution of 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, this stage is divided into multiple windows of different lengths, corresponding to different aerobic control targets.

[0030] Furthermore, the determination of the target dissolved oxygen range is mainly based on the initial sewage treatment control instruction, and the target dissolved oxygen range is preset according to the characteristics of different treatment process stages during the system design phase; specifically: For the influent pretreatment stage, the main objective is to remove large particulate suspended solids and part of the organic matter, and the dissolved oxygen demand is relatively low. The target range is set at 1.5 - 2.5 mg / L to maintain basic microbial activity; For the biochemical reaction stage, different target values 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 the full 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 an anoxic environment, the target dissolved oxygen is strictly controlled at 0.2 - 0.8 mg / L to promote denitrification reaction; For the sludge-water separation stage, a relatively low DO level needs to be maintained to promote sludge sedimentation, and the target range is set at 1.0 - 2.0 mg / L; Based on the real-time monitored influent water quality and treatment stage, the corresponding target DO range is automatically matched, and a smooth transition between different stages is achieved through a fuzzy logic algorithm.

[0031] On the other hand, the aeration energy consumption constraint also originates from the initial control instruction and is formulated based on the overall energy efficiency target of the sewage treatment plant and the operating characteristics of the equipment. The aeration energy consumption constraint includes: Fan frequency range: Based on the physical characteristics of the fan and the requirements for safe operation, the minimum frequency and the maximum frequency are set; the fan is a key device in the aeration system that provides air, and the fan frequency directly affects the aeration volume; the range of the fan frequency is specified. For example, the minimum frequency cannot be lower than a certain value to ensure the basic aeration effect, and the maximum frequency cannot exceed the rated frequency of the equipment. At the same time, in different time windows or water quality conditions, the fan frequency range is dynamically adjusted according to the energy consumption target and the dissolved oxygen demand to ensure that while meeting the sewage treatment requirements, the energy consumption of the fan operation is controlled within a reasonable range; Valve opening range: According to the design of the aeration pipeline system, the minimum opening and the maximum opening are set; the valve is used to regulate the air flow into the aeration tank, and the valve opening size determines the air flow rate; the range of the valve opening is set, such as the minimum opening ensures a certain amount of air intake, and the maximum opening cannot exceed the limit position of the valve; considering the real-time dissolved oxygen gap and the energy consumption constraint comprehensively, the valve opening is adjusted under different working conditions so that the valve opening is within the range that can meet both the dissolved oxygen demand and the energy consumption target; Energy consumption target per unit of water: Combining with the energy efficiency indicators of the sewage treatment plant, set the upper limit of aeration energy consumption per ton of water, and allocate energy consumption quotas according to time windows or process stages; Set the energy consumption target allowed for treating per unit of water according to the overall energy consumption plan and actual operation conditions of the sewage treatment plant. For example, the energy consumption for treating one cubic meter of sewage shall not exceed a certain value; Determine and adjust by combining factors such as influent water quality, process stages, and equipment operation efficiency; During actual operation, through adjusting parameters such as fan frequency and valve opening, and optimizing the control of the entire aeration system, ensure that the actual energy consumption per unit of water does not exceed the set energy consumption target, so as to achieve effective control of energy consumption.

[0032] In this embodiment, reasonable division of time windows, especially the setting of small windows during periods of drastic load changes, can capture changes in water quality and load more timely, make rapid responses, improve the accuracy of dissolved oxygen control, avoid the lag of control instructions, and effectively solve the problem that control lags behind actual water quality fluctuations in existing methods.

[0033] In some embodiments, the real-time dissolved oxygen gap is a quantitative indicator reflecting the difference between the current dynamic aerobic demand of water quality and the process preset target. Its calculation object is the dynamic dissolved oxygen range and the target dissolved oxygen range. The gap calculation needs to consider the differences between the upper and lower limits of the range simultaneously to form a two-way quantitative result. The specific steps are as follows: Extract the lower limit value (DO_dynamic_min) and upper limit value (DO_dynamic_max) of the dynamic dissolved oxygen range, and 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; Calculate the lower limit gap and upper limit gap. The lower limit gap (ΔDO_min): Reflects the difference between the actual aerobic demand lower limit and the target lower limit. The calculation formula is: ΔDO_min = DO_dynamic_min - DO_target_min. If the result is positive, it indicates that the dynamic demand lower limit is higher than the target lower limit; if it is negative, it means that the dynamic demand lower limit is lower than the target lower limit; The upper limit gap (ΔDO_max) reflects the difference between the actual aerobic demand upper limit 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; Combined with the signs and absolute value magnitudes of the lower and upper limit gaps, the current dissolved oxygen supply-demand state is judged. When ΔDO_min ≥ 0 and ΔDO_max ≥ 0, the dynamic range is completely higher than the target range, there is an aerobic excess 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 aerobic 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, the actual supply-demand matching degree is evaluated through the proportion of the overlapping interval, and the gap is mainly based on the boundary difference of the uncovered part.

[0034] 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 switches with the time window to ensure that the gap calculation synchronously reflects the difference between the latest aerobic state and the control target of the current stage, avoiding the lag problem of a single fixed value in the traditional method; the lower limit gap and the upper limit gap respectively correspond to the minimum and maximum adjustment requirements 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, the aeration volume needs to be increased 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, the aeration volume needs to be reduced to avoid excessive consumption. Through the above calculation logic, the abstract supply-demand difference is converted into a quantifiable control signal, providing a direct basis for the precise adjustment of the subsequent aeration system, solving the problems of empirical adjustment or single-point feedback lag in the existing methods, realizing the dynamic matching of dissolved oxygen supply and biochemical reaction demand, and taking into account the processing efficiency and energy consumption optimization.

[0035] 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; on the premise of meeting the energy consumption constraint, the operating parameters of the aeration equipment are adjusted to make the dissolved oxygen concentration match the biochemical reaction demand; the aeration control parameters include the fan frequency, valve opening degree, and sludge return ratio; the specific coupled analysis is as follows: According to the sign and absolute value of the difference between the lower and upper limits of the real-time dissolved oxygen gap, the urgency of the current supply-demand state is determined; when the lower limit gap ΔDO_min is negative and the 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 aerobic deficiency is high, triggering a priority oxygen supplementation instruction; when the upper limit gap ΔDO_max is positive and the dynamic range is completely higher than the target range, it is judged as aerobic excess, triggering a frequency reduction and throttling instruction; if the gap is in the overlapping interval, the adjustment weight is allocated according to the difference ratio of the uncovered boundary to ensure that the aeration volume precisely matches the gap shape. Call the aeration energy consumption constraint parameters within the current time window, including the allowable range of fan frequency, the limit of valve opening, and the energy consumption quota per unit of water volume, as the boundary conditions for the adjustment operation; for the oxygen supplementation scenario, calculate the theoretical fan frequency increase value or valve opening expansion value corresponding to the required aeration increment, and compare it with the maximum allowable value in the energy consumption constraint. If the theoretical value exceeds the upper limit, use the constraint upper limit value as the adjustment benchmark, and allocate the collaborative increment of multiple devices through an optimization algorithm; for the energy consumption reduction scenario, dynamically adjust the adjustment amplitude according to the remaining amount of the energy consumption quota. If the energy consumption per unit of water volume in the current window is already close to the upper limit, preferentially select to reduce the valve opening rather than the fan frequency to maintain the stability of the fan operation. Construct an optimization model with the minimization of the 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. The constraint conditions cover the physical limits of the equipment, the energy consumption quota of the window, and the biochemical reaction requirements; use the linear weighted method or Pareto front analysis to dynamically allocate the priority weights of treatment effect and energy consumption. For example, when the effluent water quality is critically exceeded, give priority to closing the dissolved oxygen gap; when an energy consumption overrun warning occurs, focus on the rigid implementation of the constraint conditions; combine the real-time system state and call the preset rule library for quick decision-making; for example, when ΔDO_min is -0.8 mg / L and the remaining energy consumption quota is sufficient, increase the fan frequency by 20% of the maximum allowable value and simultaneously open the valve to the middle opening; if the quota is insufficient, start the gradient adjustment mode and gradually approach the closing of the gap.

[0036] Furthermore, to avoid frequent start-stop of equipment or sudden parameter changes, smooth the optimization results and remember the historical state; based on the fan frequency and valve opening of the previous control cycle, set the upper limit of the single adjustment amplitude, such as the fan frequency change not exceeding ±5 Hz / minute, and the valve opening adjustment step size ≤ 10%; at the same time, introduce a feed-forward compensation mechanism to predict the next cycle gap trend according to the set of sequential water quality characteristics and pre-adjust the aeration intensity; for example, if the integrated trend of ammonia nitrogen indicates that the oxygen demand will continue to rise, reserve 5% of the aeration margin in the current adjustment.

[0037] Link and adjust the sludge return ratio according to the deviation between the real-time value of the mixed liquor suspended solid concentration MLSS and the target range; when MLSS is lower than the threshold and the dissolved oxygen gap is negative, moderately increase the return ratio to increase the activated sludge concentration, indirectly improve the oxygen utilization efficiency, and reduce the pure aeration oxygen supplementation demand; if the oxygen transfer efficiency decreases due to too high MLSS, reduce the return ratio and preferentially compensate for the oxygen demand gap through the aeration system to avoid excessive sludge concentration aggravating energy consumption loss.

[0038] In this embodiment, by dynamically evaluating the matching relationship between the dissolved oxygen gap and the energy consumption constraint, multi-objective collaborative optimization is achieved, with dynamic response capabilities and multi-parameter collaborative advantages. It can accurately identify the risks of insufficient or excessive oxygen demand and trigger hierarchical instructions, construct a constraint boundary by combining the physical limits of aeration equipment and energy consumption quotas, avoid equipment losses and sharp increases in energy consumption caused by over-limit regulation, use linear weighting and Pareto front analysis to balance the treatment effect and energy consumption cost, minimize the aeration energy consumption on the premise of ensuring the effluent water quality, suppress parameter mutations through a feed-forward compensation mechanism and historical state memory, combine MLSS linkage regulation to improve the oxygen mass transfer efficiency, reduce the dependence on pure aeration, enhance the adaptability of the system to load fluctuations, and its hierarchical decision-making and gradient regulation mode effectively balance the regulation accuracy and equipment life.

[0039] As Figure 2 shown, the dissolved oxygen control system of the present invention applied to the aeration system specifically includes the following modules; A data processing module, which is used to collect the water quality parameter information of the inlet and inside of the aeration tank based on a preset data collection frequency, and arrange at least two groups of water quality parameter information in time series to generate a time series water quality feature set; A dynamic oxygen demand assessment module, which is used to dynamically assess the oxygen demand of the time series water quality feature set to obtain a dynamic dissolved oxygen range; the dynamic dissolved oxygen range can accurately represent the demand intensity of the current water quality for dissolved oxygen; A target parameter determination module, which is used to divide multiple time windows according to the initial sewage treatment control instruction, and determine the corresponding target dissolved oxygen range and aeration energy consumption constraint for each time window, so as to clarify the goals and limiting conditions for the subsequent control strategy formulation; A gap calculation module, which 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, and quantify the difference between the current actual demand and the target; A control module, which is used to perform a coupling analysis on the real-time dissolved oxygen gap and the aeration energy consumption constraint, obtain real-time aeration control parameters, and adjust the aeration system accordingly to achieve precise control of the aeration system to meet the dynamic demand for dissolved oxygen in the sewage treatment process.

[0040] In this embodiment, the refined dynamic control of the aeration process is achieved through the cooperation of multiple modules, which deeply integrates the analysis of temporal water quality characteristics and the multi-time window target management, and solves the problem of control lag caused by the traditional system's dependence on fixed set values or instantaneous feedback. The data processing module captures the continuous change trend of the influent load through high-frequency and multi-parameter temporal integration, providing a data basis for dynamic aerobic demand assessment. The dynamic aerobic demand assessment module outputs the dynamic dissolved oxygen range based on the temporal characteristics, predicting in advance the demand trend of biochemical processes such as nitrification reaction for DO, breaking through the limitation of only relying on the current DO measurement value. The target parameter determination module decomposes the long-term regulation task into short-term sub-goals, sets different DO targets and energy consumption constraints in combination with the operation plan of the sewage treatment plant, and realizes the dynamic balance between treatment demand and economy. The gap calculation module accurately identifies the risks of under-aeration or over-aeration by quantifying the deviation between the real-time demand and the target, providing a decision-making basis for the control module. The control module adopts a multi-objective optimization algorithm, taking into account the energy consumption constraint while meeting the real-time gap compensation. By adjusting the linkage of multiple parameters such as the fan frequency and valve opening, it not only avoids the ultra-high energy consumption compensation in the lag phase but also prevents the energy waste caused by over-aeration.

[0041] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A dissolved oxygen control method applied to an aeration system, characterized in that, The method includes: Based on a preset data collection frequency, obtaining water quality parameter information of the influent and inside the aeration tank, and arranging at least two groups of water quality parameter information in time sequence to obtain a time-sequence water quality feature set; Performing dynamic aerobic demand assessment on the time-sequence water quality feature set to obtain a dynamic dissolved oxygen range; According to the initial sewage treatment control instruction, dividing multiple time windows and determining the target dissolved oxygen range and aeration energy consumption constraint within each time window; Calculating the real-time dissolved oxygen gap between the dynamic dissolved oxygen range and the target dissolved oxygen range within the corresponding time window; Performing coupled analysis on 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.

2. The dissolved oxygen control method applied to an aeration system according to claim 1, characterized in that, The water quality parameter information includes chemical oxygen demand, ammonia nitrogen content, mixed liquor suspended solid concentration, and water body temperature.

3. The dissolved oxygen control method applied to an aeration system according to claim 2, characterized in that, The aeration control parameters include fan frequency, valve opening, and sludge return ratio.

4. The dissolved oxygen control method applied to an aeration system according to claim 2, characterized in that, Performing dynamic aerobic demand assessment on the time-sequence water quality feature set includes: Based on the degradation kinetic model of chemical oxygen demand, calculating the oxygen demand for organic matter degradation; Calculating the oxygen demand in the nitrification stage according to the ammonia nitrogen accumulation amount and temperature correction factor; Superposing the oxygen demand for organic matter degradation and the oxygen demand in the nitrification stage, and introducing the inhibition factor of the mixed liquor suspended solid concentration on the oxygen transfer efficiency to obtain the dynamic aerobic demand intensity.

5. The dissolved oxygen control method applied to an aeration system according to claim 4, characterized in that, The calculation formula for the oxygen demand for organic matter degradation is: ThOD_COD = COD × (1 - η) × α; Wherein, ThOD_COD represents the amount of oxygen required to degrade organic matter in the influent; COD represents the measured concentration value of chemical oxygen demand in the influent; η represents the COD removal efficiency; α is the oxygen conversion coefficient, representing the amount of oxygen required for complete oxidation of unit mass of COD.

6. The dissolved oxygen control method applied to an aeration system according to claim 5, characterized in that, The calculation formula for the oxygen demand in the nitrification stage is: ThOD_NH3 = ∫NH3-N × 4.57 × (1 + 0.03 × (T - 20)); Wherein, 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, representing the ammonia nitrogen accumulation amount; 4.57 is the theoretical oxygen demand coefficient for complete nitrification of ammonia nitrogen; T represents the water body temperature in the aeration tank; 0.03 is the temperature correction factor, representing the adjustment ratio of the oxygen demand for every 1°C deviation of the temperature from the reference temperature.

7. The dissolved oxygen control method applied to an aeration system according to any one of claims 1-6, characterized in that, The aeration energy consumption constraint includes a fan frequency range, a valve opening range, and a target energy consumption per unit water volume.

8. A dissolved oxygen control system applied to an aeration system, characterized in that, The system includes: A data processing module, configured to collect water quality parameter information of the influent and inside the aeration tank based on a preset data collection frequency, and arrange at least two groups of water quality parameter information in time sequence to generate a time-sequence water quality feature set; A dynamic aerobic demand assessment module, configured to perform dynamic aerobic demand assessment on the time-sequence water quality feature set to obtain a dynamic dissolved oxygen range; A target parameter determination module, configured to divide multiple time windows according to the initial sewage treatment control instruction, and determine the corresponding target dissolved oxygen range and aeration energy consumption constraint for each time window; A gap calculation module, configured 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; A control module for performing a coupled analysis on the real-time dissolved oxygen gap and the aeration energy consumption constraint to obtain real-time aeration control parameters and adjusting the aeration system based on the parameters.

9. An electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and operable on the processor, the transceiver, the memory, and the processor are connected through the bus, characterized in that, When the computer program is executed by the processor, it implements the steps in the method according to any one of claims 1-7.

10. A computer-readable storage medium, on which a computer program is stored, characterized in that,When the computer program is executed by the processor, it implements the steps in the method according to any one of claims 1-7.

Images