Gradient optimization control method for aeration system of sewage plant

Abstract

The invention discloses a gradient optimization control method for an aeration system of a sewage plant. The gradient optimization control method comprises the following steps: S1, acquiring key monitoring multi-source data of the aeration system of the sewage plant in real time through an online sensor; s2, setting a security threshold value of each monitoring data to form a security constraint condition; s3, taking unit nitrogen removal energy consumption minimization as an optimization target, and constructing a comprehensive loss function fusing an energy consumption item and a water quality deviation item; s4, iteratively updating the air volume of the aeration system by adopting a self-adaptive gradient algorithm; s5, the optimized and updated air volume is issued to the aeration equipment to be executed, and water quality data and energy consumption data after execution are collected in real time, a control effect is calculated, an award signal is generated, and the award signal serves as feedback to be input into an optimization module; and S6, dynamically adjusting key parameters including the learning rate and the gradient threshold according to a feedback result. According to the invention, the energy consumption can be effectively reduced and the control precision can be improved while ensuring that the effluent stably reaches the standard, and the stable operation of the aeration system of the sewage plant under a complex working condition is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of sewage treatment technology and intelligent control, and particularly relates to a gradient optimization control method for an aeration system of a sewage plant, which is suitable for aeration air volume regulation and control of core process units such as aeration tanks and aerobic sections of the sewage plant. BACKGROUND

[0002] The aeration system is the most core oxygen supply link in the biochemical treatment unit of the sewage treatment plant, and its operation energy consumption accounts for a dominant position in the total energy consumption of the whole plant, and is a key factor affecting the overall energy efficiency level of the sewage plant.

[0003] The essence of the aeration process is to transport air to the water body by a blower in the aeration tank or the aerobic section, to provide sufficient dissolved oxygen for the microorganisms in the activated sludge system, so as to ensure the effective degradation of organic matter and the removal of pollutants such as nitrogen and phosphorus.

[0004] Since the aeration process is directly related to the effluent quality and operation cost of the sewage plant, its control strategy and operation efficiency have always been the focus of attention in the field of sewage treatment. However, the oxygen demand of the aeration process is affected by multiple factors such as influent water quality, water quantity fluctuation, load change and equipment operation state, and presents significant dynamic characteristics.

[0005] In actual operation, the pollutant concentration and flow rate in the influent will change greatly over time, and the water temperature under different seasons and weather conditions will also change the metabolic rate of microorganisms, resulting in continuous fluctuation of dissolved oxygen demand. Under such complex and variable conditions, the traditional fixed set value control, single dissolved oxygen feedback control or segmented control method is difficult to balance the stable standard of effluent quality and the optimal utilization of energy consumption.

[0006] On the one hand, a conservative control strategy usually maintains a high aeration amount to avoid water quality exceeding the standard, but this will cause long-term energy waste; on the other hand, the control system often lags in response when facing dramatic changes in working conditions, which can easily lead to insufficient aeration or excessive aeration, affecting the stability of the effluent and increasing the operation cost.

[0007] Most existing aeration control methods lack online self-optimization and parameter adaptive adjustment capabilities, and cannot automatically correct the control strategy according to real-time operation data. When the influent water quality or working conditions change continuously, these methods often need to rely on manual intervention and experience for adjustment, which is low in efficiency and subjective, and is difficult to achieve long-term efficient and stable operation.

[0008] In terms of the coordinated optimization of energy consumption and water quality, most existing methods are single-target oriented, often prioritizing the stable standard of effluent quality at the expense of energy consumption economy, or excessively pursuing energy saving at the expense of water quality safety, lacking dynamic evaluation and optimization mechanism for "unit pollutant removal energy consumption".

[0009] In addition, aeration systems also face the problem of declining adaptability to operating conditions during long-term operation. Seasonal changes, changes in sludge properties, and long-term drift of influent water quality can all cause the control algorithm with fixed parameters to gradually lose its optimization ability, thereby reducing control accuracy and operational stability.

[0010] To overcome the limitations of traditional control modes in terms of energy consumption, accuracy, and adaptability, and to improve the intelligent and efficient operation of wastewater treatment plants, it is necessary to provide an intelligent control method for aeration systems with an adaptive optimization strategy. Summary of the Invention

[0011] The purpose of this invention is to provide a gradient optimization control method for aeration systems in wastewater treatment plants, which solves the problems of low control accuracy, high energy consumption, poor adaptability, and difficulty in balancing water quality and energy consumption in existing wastewater treatment plant aeration control methods.

[0012] To address this, the present invention provides a gradient optimization control method for a wastewater treatment plant aeration system, comprising the following steps: S1, real-time acquisition of key monitoring multi-source data of the wastewater treatment plant aeration system through online sensors, including influent and effluent water quality parameters, aeration tank status parameters, and equipment operating parameters, with sampling frequencies including multiple time scales; S2, setting safety thresholds for each monitoring data to form safety constraints and ensure that the wastewater treatment plant aeration system operates within a safe boundary; S3, constructing a comprehensive loss function that integrates energy consumption and water quality deviation terms, with the minimization of energy consumption per unit nitrogen removal as the optimization objective; S4, under the premise of meeting the safety thresholds, using the minimum energy consumption of the aeration system as the objective function, iteratively updating the aeration system airflow using an adaptive gradient algorithm; S5, distributing the optimized and updated airflow to the aeration equipment for execution, calculating the control effect and generating a reward signal by real-time acquisition of water quality and energy consumption data after execution, which serves as feedback input to the optimization module; S6, dynamically adjusting key parameters, including the learning rate and gradient thresholds, based on the feedback results, and automatically adjusting the core parameters of the control algorithm when the reward signal falls below the set threshold or the operating indicators fluctuate beyond the limit.

[0013] This invention constructs a control boundary that balances safety and efficiency by fusing multi-source data and setting dynamic thresholds. It quantifies the target of "energy consumption per unit nitrogen removal" to establish a loss function that balances water quality compliance and energy consumption optimization. It uses an adaptive gradient algorithm to optimize aeration volume in real time to improve control accuracy. Furthermore, it dynamically adjusts algorithm parameters through a feedback mechanism to enhance the system's adaptability to complex operating conditions. This breakthrough overcomes the limitations of traditional control methods under dynamic conditions and enables precise, energy-saving, and stable operation of the aeration system.

[0014] Compared with traditional fixed setpoint or single feedback control, this invention significantly improves dynamic adaptability, energy saving, control accuracy, and operational robustness. It can maintain stable and efficient operation under complex and variable operating conditions, and has good engineering deployability and application value. It is especially suitable for wastewater treatment plants with large fluctuations in influent conditions and complex operating conditions.

[0015] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0016] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0017] Figure 1 This is a flowchart of the gradient optimization control method for the aeration system of a wastewater treatment plant according to the present invention. Detailed Implementation

[0018] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] This invention focuses on the core objective of "achieving optimal energy consumption and precise control of the aeration system while ensuring stable and compliant effluent quality," and constructs a comprehensive optimization framework that integrates energy consumption and water quality deviation.

[0020] This method uses multi-source high-frequency sensors to collect influent and effluent water quality, aeration tank status, and equipment operating parameters in real time. It also combines process mechanisms and expert experience to set a safety constraint library to ensure that the system operates within safe boundaries. Based on this, a comprehensive loss function is constructed with minimizing energy consumption per unit of nitrogen removal as the core, dynamically balancing the dual objectives of energy saving and water quality compliance. An adaptive gradient optimization algorithm is used to iteratively update the aeration air volume, and projection calculations are used to ensure that the control variables are always within a safe range, thus achieving high-precision real-time control of the aeration system.

[0021] After control is executed, the system generates a reward signal based on the deviation between water quality and energy consumption, which drives the optimization module to continuously correct the control strategy. Through adaptive parameter updates and evolutionary strategy optimization mechanisms, the system has the ability to learn and improve its performance over a long period of time.

[0022] Specifically, see Figure 1 The gradient optimization control method for the wastewater treatment plant aeration system of the present invention includes the following steps:

[0023] S1, Real-time Data Acquisition

[0024] The system collects key monitoring data from multiple sources in real time from online sensors, including influent and effluent water quality parameters (COD, ammonia nitrogen (NH3-N), total nitrogen (TN), total phosphorus (TP), etc.), aeration tank status parameters (dissolved oxygen (DO), sludge concentration (MLSS), pH value, etc.), and equipment operating parameters (blower speed, valve opening, power, etc.). The sampling frequency can be set to various time scales such as 1 minute, 10 minutes, and 30 minutes to ensure the system obtains high-timeliness and high-resolution operational data. The collected data is transmitted to the central control system via industrial Ethernet or wireless transmission, where data cleaning, outlier removal, and missing value imputation are performed to ensure the accuracy and completeness of the input data.

[0025] S2, Safety Threshold Setting

[0026] Based on the operating principles of wastewater treatment process units and expert experience, safety thresholds for various monitoring data are set to form a safety constraint library, ensuring that the wastewater treatment plant's aeration system operates within safe boundaries. The threshold settings in this safety constraint library are not only based on expert experience and process principles but are also periodically optimized and adjusted based on statistical analysis results of actual operating data. The optimization objectives are determined to be minimizing energy consumption and achieving wastewater treatment standards, where wastewater treatment effectiveness is measured by effluent water quality indicators. The set constraints include the upper and lower limits of aeration equipment operating parameters, as well as reasonable ranges for water quality parameters. By setting these thresholds, the safe and stable operation of the system is ensured, providing a safety boundary for subsequent optimization and control.

[0027] S3. Construction of the objective function

[0028] A loss function is constructed with the goal of minimizing energy consumption per unit of nitrogen removal. When constructing the objective function, energy consumption factors during the aeration process are comprehensively considered, including the electrical energy consumption of blowers, aeration equipment, and their auxiliary equipment. Energy consumption is calculated by summing the products of equipment power and operating time. Simultaneously, the wastewater treatment effect is accurately assessed, with effluent nitrogen content (ammonia nitrogen, total nitrogen, etc.) used as a key indicator to ensure effective energy reduction while meeting effluent standards.

[0029] The constructed objective function includes control variables, state variables, and constraints. Through analysis of historical data and actual operational results, the weighting coefficients are dynamically adjusted to balance the relationship between energy consumption reduction and water quality compliance. The objective function focuses on minimizing energy consumption per unit of nitrogen removal, incorporating a weighted calculation of energy consumption and water quality deviation. The calculation formula is as follows:

[0030]

[0031]

[0032] ΔN TN =(c in,TN -cout,TN )·Q in ·Δt---------------------------------------------(3)

[0033] Where w1 is the weighting coefficient of the energy consumption item, E aeration The total energy consumption of the aeration system is expressed as ΔN (kWh). TN w2 represents the total nitrogen removal per unit time (kg), w2 represents the weighting coefficient of the water quality deviation term, and α represents the total nitrogen removal per unit time (kg). i c represents the indicator weight. i,actual c represents the real-time monitored concentration of effluent indicators (mg / L). i,standard P represents the standard value for effluent discharge. fan (t) represents the actual power of the wind turbine at time t (kW), Δt represents the sampling interval (h), and c in,TN c out,TN Q represents the total nitrogen concentration (mg / L) in the influent and effluent. in Indicates influent flow rate (m³) 3 / h).

[0034] S4, Gradient Optimization Control

[0035] Under the premise of meeting the safety threshold, the minimum energy consumption of the aeration system is taken as the objective function, and the air volume of the aeration system is iteratively updated using an adaptive gradient algorithm. The gradient descent algorithm is used to solve the optimization objective, calculating the gradient of the objective function with respect to control variables (such as fan speed and valve opening). The learning rate is adaptively adjusted to update the control variables, so that the objective function gradually decreases along the gradient descent direction, thereby achieving energy reduction and improved wastewater treatment effect. During the optimization process, the constraints are strictly followed to ensure that the control variables always remain within the feasible region. The formula for the adaptive gradient algorithm is as follows:

[0036]

[0037] m k =β1m k-1 +(1-β1)g k ------------------------------------(5)

[0038]

[0039]

[0040] Among them, g k This indicates that the objective function L is related to the aeration volume in the k-th iteration. The gradient, m k v represents the mean of the first moment of the gradient. kThe second moment variance of the gradient is represented by the attenuation coefficients β1 and β2, which are used to smoothly accumulate historical gradient information. This represents the iterative update of air volume and safety constraints, expressed through the learning rate α and the bias correction term. And by eliminating the zero-term ε, adaptive adjustment of airflow is achieved. This indicates a projection operation, ensuring that the updated airflow remains within the safe threshold range.

[0041] S5. Control Execution and Feedback

[0042] The optimized and updated air volume control is sent to the aeration equipment for execution. By collecting water quality data and energy consumption data in real time after execution, the control effect is calculated and a reward signal is generated as feedback input to the optimization module.

[0043] Specifically, the optimized and updated airflow control is rapidly sent to the aeration equipment for execution. High-precision sensors monitor key performance indicators in real time after execution, including changes in dissolved oxygen (DO) in the aeration tank and energy consumption data. The deviation ΔQ between the actual monitored value and the target value is accurately calculated, and this deviation is promptly input into the optimization module as a reward signal.

[0044] Simultaneously, the actual energy consumption and nitrogen removal amount during each iteration are recorded in detail to form historical data records, providing data support for subsequent system performance evaluation and optimization strategy adjustments. The calculation formula for the reward signal fed back by the optimization module is as follows:

[0045] ΔQ DO =|DO real -DO target |------------------------------------(8)

[0046] ΔQ E =|E real -E target |-------------------------------------------(9)

[0047]

[0048] Among them, DO real Dissolved oxygen concentration (mg / L) is monitored in real time; DO target Set the dissolved oxygen concentration (mg / L) for the target; ΔQ DO E represents the water quality deviation value. real The actual monitored energy consumption (kWh); E target Target energy consumption (kWh); ΔQ Eλ1 and λ2 are the energy consumption deviation values; ΔQ is the overall performance deviation; λ1 and λ2 are the weighting coefficients of water quality and energy consumption, satisfying λ1 + λ2 = 1; R t This is a reward signal used to provide feedback to the optimization module.

[0049] S6, Adaptive Parameter Update

[0050] The adaptive parameter update mechanism dynamically adjusts key optimization parameters such as the learning rate and gradient threshold based on feedback results. When the reward signal falls below a preset threshold or system operating indicators fluctuate beyond limits, an online hyperparameter searcher is automatically triggered to iteratively optimize and correct the learning rate and weight coefficients through an evolutionary strategy. Conversely, when the system is operating stably and water quality indicators meet standards, the optimization frequency is appropriately reduced to minimize fluctuations and extend equipment life. This mechanism combines short-term feedback adjustment with long-term trend optimization to achieve stable and efficient aeration control under complex and variable operating conditions, and endows the system with continuous self-learning and robust operation capabilities.

[0051] This invention demonstrates its superiority over existing methods from four dimensions. Specific performance comparison indicators are shown in the table below. Method 1 is traditional fixed setpoint control, Method 2 is traditional feedback control, and Method 3 is the gradient optimization control of this invention. The results show the performance improvement of Method 3 compared to Method 2.

[0052]

[0053] As can be seen from the above data, in terms of dynamic adaptability, this invention, facing disturbances in influent water quality (such as TN fluctuations of ±30%) and environmental factors (such as water temperature changes of 5℃), improves the water quality compliance rate to 96% through real-time acquisition of multi-source data (multi-time scales of 1-30 minutes) and adaptive gradient adjustment, which is 23.1% higher than the traditional single feedback control; the air volume adjustment lag time is shortened to 8 minutes, which is 71.4% lower than the traditional method, and can quickly respond to fluctuations in operating conditions.

[0054] In terms of energy conservation and consumption reduction, the core optimization target is energy consumption per unit of nitrogen removal. By balancing energy consumption and water quality through a comprehensive loss function, the energy consumption per unit of nitrogen removal in actual operation is reduced to 4.1 kWh / kg·TN, which is 36.9% less than that of traditional single feedback control. The average daily total energy consumption of aeration is reduced by 31.4%, and the annual electricity cost can be saved by about 120,000 yuan (calculated at an industrial electricity price of 0.6 yuan / kWh).

[0055] In terms of control precision, with the help of adaptive gradient algorithm iterative optimization and projection operation constraints (ensuring that the air volume is within the safe threshold), the DO control deviation is reduced to ±0.2mg / L, which is 60% higher than the traditional single feedback control; the effluent TN compliance deviation rate is reduced to 0.9mg / L, avoiding the risk of water quality exceeding the standard due to insufficient control precision.

[0056] In terms of operational robustness, by controlling execution feedback (driven by reward signals) and adaptive parameter updates (dynamic adjustment of learning rate and gradient threshold), the number of consecutive 30-day operation index fluctuations exceeding the limit is only 3 hours, which is 80% less than traditional single feedback control; the frequency of equipment start-up and shutdown is reduced to 3 times / day, reducing equipment wear and extending the service life of core equipment such as fans by about 2-3 years.

[0057] This method can maintain stable and efficient operation under complex and variable operating conditions, and has good engineering deployability and application value. It is especially suitable for wastewater treatment plants with large fluctuations in influent conditions and complex operating conditions.

[0058] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A gradient optimization control method for an aeration system in a wastewater treatment plant, characterized in that, Includes the following steps: S1. Real-time collection of key monitoring multi-source data of the wastewater treatment plant aeration system through online sensors, including influent and effluent water quality parameters, aeration tank status parameters, and equipment operating parameters, with sampling frequencies including multiple time scales; S2. Set safety thresholds for each monitoring data to form safety constraints and ensure that the aeration system of the wastewater treatment plant operates within the safety boundary; S3 takes minimizing the energy consumption per unit nitrogen removal as the optimization objective and constructs a comprehensive loss function that integrates energy consumption and water quality deviation terms; S4. Under the premise of meeting the safety threshold, the air volume of the aeration system is iteratively updated using an adaptive gradient algorithm with the minimum energy consumption of the aeration system as the objective function. S5. The optimized and updated air volume is sent to the aeration equipment for execution. The water quality data and energy consumption data after execution are collected in real time to calculate the control effect and generate a reward signal as feedback input to the optimization module. S6. Based on the feedback results, the key parameters, including the learning rate and gradient threshold, are dynamically adjusted. When the reward signal is lower than the set threshold or the fluctuation of the operating indicators exceeds the limit, the core parameters of the control algorithm are automatically adjusted.

2. The gradient optimization control method for a wastewater treatment plant aeration system according to claim 1, characterized in that, The influent and effluent water quality parameters include COD, ammonia nitrogen, TN, and TP. The aeration tank status parameters include dissolved oxygen (DO), sludge concentration (MLSS), and pH value. The equipment operating parameters include blower speed, valve opening degree, and power.

3. The gradient optimization control method for a wastewater treatment plant aeration system according to claim 1, characterized in that, The various time scales include 1 minute, 10 minutes, and 30 minutes.

4. The gradient optimization control method for a wastewater treatment plant aeration system according to claim 1, characterized in that, In step S2, the constraints include the upper and lower limits of the operating parameters of the aeration equipment and the reasonable range of water quality parameters.

5. The gradient optimization control method for a wastewater treatment plant aeration system according to claim 1, characterized in that, In step S3, the loss function F (x) With minimizing energy consumption per unit of nitrogen removal as the core objective, a weighted calculation combining energy consumption and water quality deviation is employed, as shown in the formula: ΔN TN =(c in,TN -c out,TN )·Q in ·Δt Where w1 is the weighting coefficient of the energy consumption item, E aeration ΔN represents the total energy consumption of the aeration system (kWh). TN w2 represents the total nitrogen removal per unit time (kg), w2 represents the weighting coefficient of the water quality deviation term, and α represents the total nitrogen removal per unit time (kg). i This indicates the weight of the indicators, which include the concentrations of nitrogen and phosphorus in the effluent. i,actual c represents the real-time monitored concentration of effluent indicators (mg / L). i,standard This indicates the effluent discharge standard value, P. fan (t) represents the actual power of the wind turbine at time t (kW), Δt represents the sampling interval (h), and c in,TN c out,TN Q represents the total nitrogen concentration (mg / L) in the influent and effluent. in Indicates influent flow rate (m³) 3 / h).

6. The gradient optimization control method for a wastewater treatment plant aeration system according to claim 1, characterized in that, In step S4, the gradient descent algorithm is used to solve the optimization objective and calculate the gradient of the objective function with respect to the control variables. The control variables are updated by adaptively adjusting the learning rate so that the objective function gradually decreases along the gradient descent direction, thereby reducing energy consumption and improving wastewater treatment efficiency. During the optimization process, the constraints are strictly followed to ensure that the control variables remain within the feasible region. The calculation formula for the adaptive gradient algorithm is as follows: m k =β1m k-1 +(1-β1)g k v k =β2v k-1 +(1-β2)(g k ) 2 Among them, g k This indicates that the objective function L is related to the aeration volume in the k-th iteration. The gradient, m k v represents the mean of the first moment of the gradient. k The second moment variance of the gradient is represented by the attenuation coefficients β1 and β2, which are used to smoothly accumulate historical gradient information. This represents the iterative update of air volume and safety constraints, expressed through the learning rate α and the bias correction term. And by eliminating the zero-term ε, adaptive adjustment of airflow is achieved. This indicates a projection operation, ensuring that the updated airflow remains within the safe threshold range.

7. The gradient optimization control method for a wastewater treatment plant aeration system according to claim 5, characterized in that, In step S5, the optimized and updated airflow control is rapidly sent to the aeration equipment for execution. Key performance indicators (KPIs) are monitored in real-time using high-precision sensors, including changes in dissolved oxygen (DO) in the aeration tank and energy consumption data. The deviation ΔQ between the actual monitored value and the target value is accurately calculated, including dissolved oxygen concentration deviation and energy consumption deviation. This deviation is then promptly input into the optimization module as a reward signal. Simultaneously, the actual energy consumption and nitrogen removal during each iteration are recorded in detail, forming a historical data record. The formula for calculating the reward signal fed back by the optimization module is as follows: ΔQ DO =|DO real -DO target | ΔQ E =|E real -E target | Among them, DO real Dissolved oxygen concentration (mg / L) is monitored in real time; DO target Set the dissolved oxygen concentration (mg / L) for the target; ΔQ DO E represents the water quality deviation value. real The actual monitored energy consumption (kWh); E target Target energy consumption (kWh); ΔQ E λ1 and λ2 are the energy consumption deviation values; ΔQ is the overall performance deviation; λ1 and λ2 are the weighting coefficients of water quality and energy consumption, satisfying λ1 + λ2 = 1; R t This is a reward signal used to provide feedback to the optimization module.

8. The gradient optimization control method for a wastewater treatment plant aeration system according to claim 1, characterized in that, In step S6, the adaptive parameter update dynamically adjusts key parameters, including the learning rate and gradient threshold, based on the feedback results. When the reward signal is below the threshold or the fluctuation of the performance index exceeds the limit, the adaptive parameter update mechanism is triggered, and the online hyperparameter searcher performs evolutionary strategy optimization and iterative parameter updates.

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