A multi-parameter collaborative optimization-based sewage treatment aeration control system and method

Abstract

The present application provides a kind of wastewater treatment aeration control system and method based on multi-parameter collaborative optimization, the present application utilizes multi-parameter sensor module to collect the multi-sensor data of COD, BOD5, DO, ammonia nitrogen, ORP, pH, influent flow, sludge concentration of wastewater in aeration tank;And according to the relationship between dynamic correction oxygen mass transfer coefficient and feedforward parameter, establish influent load and aeration;Through PID control algorithm and feedback parameter, the fine tuning value of blower air volume and butterfly valve opening is calculated, and the total air volume target value is obtained by correcting the aeration amount;Using adaptive PID controller, according to the adaptive PID control parameter and the total air volume target value corrected, the guide vane opening and speed of blower are adjusted, and according to the gradient butterfly valve adjustment strategy, the butterfly valve opening is adjusted.The present application uses adaptive PID to optimize blower output, and dynamically adjusts the butterfly valve opening of each biological tank through gradient valve adjustment strategy, realizes precise control of aeration amount;Ensure the effect of wastewater treatment.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a wastewater treatment aeration control system and method based on multi-parameter collaborative optimization. Background Technology

[0002] Wastewater treatment is a crucial aspect of environmental protection, and the aeration process is the core step in the activated sludge process, directly impacting treatment efficiency and energy consumption. Traditional aeration control technologies primarily rely on single-parameter feedback (such as dissolved oxygen) or simple feedforward models, typically employing linear regulation, where the butterfly valve opening is linearly related to the airflow. However, this regulation method lacks sufficient control precision in high-load and low-load zones, making it difficult to meet the demands of complex operating conditions. Furthermore, it suffers from the following problems:

[0003] 1. Response lag: Traditional PID control has poor adaptability to water quality fluctuations, resulting in low DO control accuracy and excessive or insufficient aeration;

[0004] 2. High energy consumption: The blower is often in a high-load operation state, and the air main pressure is maintained at a high level, resulting in energy waste;

[0005] 3. Poor compatibility with multiple processes: Existing systems are difficult to be compatible with A. 2 Different chemical conditions such as O and oxidation ditch.

[0006] In recent years, with the development of sensor technology and artificial intelligence, multi-parameter collaborative optimization has become a research hotspot for improving aeration control accuracy. For example, CN202222679169.7 proposes an aeration control method based on hardware adjustment, but it does not involve multi-parameter fusion and dynamic oxygen mass transfer correction; CN114249436 A discloses an aeration control method based on DO feedback, but its control accuracy and energy-saving effect are limited. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a wastewater treatment aeration control system and method based on multi-parameter collaborative optimization. This invention significantly improves DO control accuracy through multi-parameter collaborative optimization and dynamic oxygen mass transfer coefficient correction. By dynamically adjusting the opening degree of butterfly valves in each biological tank through a gradient valve adjustment strategy, it achieves precise control of aeration volume and ensures wastewater treatment effectiveness.

[0008] The technical solution of this invention is: a wastewater treatment aeration control system based on multi-parameter collaborative optimization, comprising:

[0009] A multi-parameter sensor module is installed at the inlet, aeration tank and outlet to collect multi-sensor data on COD, BOD5, DO, ammonia nitrogen, ORP, pH, influent flow rate and sludge concentration of wastewater.

[0010] a dynamic oxygen mass transfer coefficient correction module, configured to calculate a dynamic correction oxygen mass transfer coefficient according to bubble image features of the aeration tank;

[0011] a feedforward control module, configured to establish a relationship between influent load and aeration according to the dynamic correction oxygen mass transfer coefficient and feedforward parameters;

[0012] a feedback control module, configured to calculate fine adjustment values of blower air volume and butterfly valve opening degree through a PID control algorithm and feedback parameters, and correct the aeration amount to obtain a total air volume target value;

[0013] an adaptive PID controller, configured to adjust the blower guide vane opening degree and speed according to the adaptive PID control parameters and the corrected total air volume target value, and adjust the butterfly valve opening degree according to a gradient butterfly valve adjustment strategy.

[0014] Preferably, the multi-parameter sensor module collects multi-sensor data, and then performs time alignment and standardization processing on the multi-sensor data, and then performs fusion by using Kalman filtering or weighted fusion algorithm.

[0015] Preferably, COD, BOD5, ammonia nitrogen, and influent flow are taken as feedforward parameters; and DO concentration, ORP, pH, sludge concentration, and effluent ammonia nitrogen are taken as feedback parameters.

[0016] Preferably, the dynamic oxygen mass transfer coefficient correction module calculates the oxygen mass transfer coefficient K La according to the following expression:

[0017] K La = α·A(t) + β·C(t) + γ·V(t) + δ

[0018] wherein K La represents the oxygen mass transfer coefficient; A(t) represents the bubble area at time t; C represents the trajectory curvature; V represents the average speed of the bubble; and α, β, γ, and δ are weight parameters of the dynamic oxygen mass transfer coefficient correction model.

[0019] Preferably, the bubble image features are extracted from the sewage images in the aeration tank collected by the high-speed camera device by using a convolutional neural network (CNN), and the extracted bubble image features include bubble area, trajectory curvature, and average speed features.

[0020] Preferably, the calculation formula of the feedforward control module for establishing the relationship between the influent load and the aeration according to the dynamic correction oxygen mass transfer coefficient and the feedforward parameters is as follows:

[0021]

[0022] wherein Q (t) represents the blower air volume at time t for feedforward control; and K La(t) is a dynamic correction oxygen mass transfer coefficient; QIN(t) is the water inflow at t; COD(t) is the oxygen demand at t; NH3_N(t) is the ammonia nitrogen concentration at t; k1, k2, k3 are feedforward control systems.

[0023] As preferred, the feedback control module calculates the calculation expression of the fine tuning value as:

[0024]

[0025] In the formula, is the fine tuning value; e(t) is the DO concentration deviation at t; K p , K i , K d PID control parameters of the PID controller; is the basic parameter of the PID controller; is the basic oxygen mass transfer coefficient.

[0026] As preferred, the calculation formula of the total air quantity target value obtained by correcting the aeration quantity of the feedback control module is:

[0027]

[0028] In the formula, is the total air quantity target value at t.

[0029] As preferred, the butterfly valve opening degree is divided into a low load area with an opening degree of 15%-30% and a high load area with an opening degree of 30%-100%.

[0030] As preferred, when DO<1.5mg / L, the butterfly valve opening degree is up-regulated at a rate of 5%-10% / min; when DO>2.5mg / L, the butterfly valve opening degree is down-regulated at a rate of 3%-8% / min.

[0031] As preferred, the present application further provides a sewage treatment aeration control method based on multi-parameter collaborative optimization, comprising the following steps:

[0032] S1), using a multi-parameter sensor module to collect multi-sensor data of COD, BOD5, DO, ammonia nitrogen, ORP, pH, water inflow, and sludge concentration of the wastewater in the aeration tank;

[0033] S2), calculating a dynamic correction oxygen mass transfer coefficient according to bubble image characteristics;

[0034] S3), establishing a relationship between water inflow load and aeration according to the dynamic correction oxygen mass transfer coefficient and feedforward parameters;

[0035] S4) The blower air volume and butterfly valve opening are fine-tuned by using PID control algorithm and feedback parameters, and the aeration volume is corrected to obtain the total air volume target value.

[0036] S5) The blower guide vane opening and speed are adjusted by using an adaptive PID controller based on the adaptive PID control parameters and the corrected total air volume target value, and the butterfly valve opening is adjusted according to the gradient butterfly valve adjustment strategy.

[0037] The beneficial effects of this invention are as follows:

[0038] 1. This invention adopts adaptive PID to optimize the blower output and dynamically adjusts the opening of the butterfly valves in each biological tank through a gradient valve adjustment strategy to achieve precise control of aeration volume;

[0039] 2. This invention improves aeration efficiency by dynamically adjusting the aeration process to control the butterfly valve opening and blower airflow.

[0040] 3. This invention significantly improves the accuracy of DO control through multi-parameter synergistic optimization and dynamic oxygen mass transfer coefficient correction, and can stably control it at a level of 1.5 to 2 mg / L, ensuring the wastewater treatment effect;

[0041] 4. This invention reduces blower energy consumption by 10%-30% by accurately predicting and optimizing aeration volume. Attached Figure Description

[0042] Figure 1 This is a framework structure diagram of the system in Embodiment 1 of the present invention;

[0043] Figure 2 This is a flowchart illustrating the method of Embodiment 2 of the present invention;

[0044] Figure 3 A curve of valve control in Embodiment 1 of the present invention;

[0045] Figure 4 A comparison chart of DO concentration before and after implementation in Example 1 of this invention. Detailed Implementation

[0046] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0047] Example 1

[0048] like Figure 1 As shown, this embodiment provides a wastewater treatment aeration control system based on multi-parameter collaborative optimization, including:

[0049] A multi-parameter sensor module is installed at the inlet, aeration tank and outlet to collect multi-sensor data on COD, BOD5, DO, ammonia nitrogen, ORP, pH, influent flow rate and sludge concentration of wastewater.

[0050] a dynamic oxygen transfer coefficient correction module, configured to calculate a dynamic correction oxygen transfer coefficient according to the bubble image features of the aeration tank;

[0051] a feedforward control module, configured to establish a relationship between the influent load and the aeration according to the dynamic correction oxygen transfer coefficient and feedforward parameters;

[0052] a feedback control module, configured to calculate fine adjustment values of the air volume of the air blower and the opening degree of the butterfly valve through a PID control algorithm and feedback parameters, and correct the aeration amount to obtain a total air volume target value;

[0053] an adaptive PID controller, configured to adjust the opening degree and rotating speed of the guide vane of the air blower according to the adaptive PID control parameters and the corrected total air volume target value, and adjust the opening degree of the butterfly valve according to a gradient butterfly valve adjustment strategy.

[0054] In this embodiment, the multi-parameter sensor module includes multiple sensors to realize real-time collection of different sewage data of the aeration tank.

[0055] In this embodiment, COD, BOD5, ammonia nitrogen and influent flow are taken as the feedforward parameters, and DO concentration, ORP, pH, sludge concentration and effluent ammonia nitrogen are taken as the feedback parameters.

[0056] Preferably, the multi-parameter sensor module collects multi-sensor data and then performs time alignment and standardization processing on the multi-sensor data. In addition, the DO concentration is fused by using a Kalman filtering algorithm, and the specific process is as follows.

[0057] 1) Time alignment

[0058] The time alignment is used for time synchronization of the multi-sensor data. Linear interpolation is used to align non-uniform sampling data. Assuming that the measurement values of sensor A at time points t1 and t2 are X A (t1) and X A (t2), and the time stamp t of sensor B is to be aligned (t1 < t < t2):

[0059]

[0060] 2) Standardization processing

[0061] Z-score standardization is used to eliminate dimension differences, and the formula is as follows:

[0062]

[0063] In the formula, X is the sensor data before standardization processing; is the sensor data after standardization processing; μ is the mean value of the parameters in the data window; and σ is the standard deviation.

[0064] ​3) Kalman filter algorithm fusion

[0065] For the measurement value of n DO sensors, weights are assigned according to sensor accuracy, and the fusion formula is:

[0066]

[0067] In the formula, x i is the measurement value of the i th DO sensor; w i is the weight of the i th DO sensor; is the historical measurement error of the i th DO sensor.

[0068] As preferred in the embodiment, the dynamic oxygen mass transfer coefficient correction module calculates the oxygen mass transfer coefficient K la The expression of K

[0069] K La = α·A(t) + β·C(t) + γ·V(t) + δ

[0070] In the formula, K La represents the oxygen mass transfer coefficient; A(t) is the bubble area at time t; C is the trajectory curvature; V is the average speed of the bubble; α, β, γ, and δ are weight parameters of the dynamic oxygen mass transfer coefficient correction model.

[0071] The bubble image features are extracted from the sewage images in the aeration tank collected by the high-speed camera through a convolutional neural network (CNN), and the extracted bubble image features include bubble area, trajectory curvature, and average speed features.

[0072] As preferred in the embodiment, the feedforward control module establishes a calculation formula for the relationship between the influent load and aeration according to the dynamically corrected oxygen mass transfer coefficient and the feedforward parameters, and the calculation formula is:

[0073]

[0074] In the formula, Q(t) is the air volume of the blower at time t; K La (t) is the dynamically corrected oxygen mass transfer coefficient; QIN(t) is the influent volume at time t; COD(t) is the oxygen demand at time t; NH3_N(t) is the ammonia nitrogen concentration at time t; k1, k2, and k3 are the feedforward control system.

[0075] As preferred in the embodiment, the feedback control module calculates the fine tuning value, and the calculation expression is:

[0076]

[0077] In the formula, is the fine tuning value; e(t) is the DO concentration deviation at time t; Kp K i K d These are the PID control parameters for the PID controller; These are the basic parameters for a PID controller; It is the basic oxygen mass transfer coefficient.

[0078] The formula for the feedback control module to correct the aeration rate is as follows:

[0079]

[0080] In the formula, The total air volume target value after correction at time t.

[0081] In this preferred embodiment, the valve opening is finely adjusted within a small range, specifically for a low-load zone with an opening of 15%-30% and a high-load zone with an opening of 30%-100%. This embodiment divides the butterfly valve opening into 5 levels: lower limits of 15%, 17%, 20%, 27%, and 30%-100%. Each opening level corresponds to an airflow gradient of ±130m³ / s. 3 / h; such as Figure 3 As shown, the response time is set to 10 minutes. The effective adjustment range is within 30%, and the adjustment capability is lost between 30% and 100%. When DO < 1.5 mg / L, the butterfly valve opening degree is increased by 5%-10% / min; when DO > 2.5 mg / L, the butterfly valve opening degree is decreased by 3%-8% / min.

[0082] To achieve uniformly gradual airflow adjustment with each valve adjustment, the valve opening process is as follows: the upper limit of the valve opening is 100%, and the lower limit is 15%. The airflow change gradient is typically set to five levels; here, five airflow change gradients are used: 2980, 3110, 3240, 3370, and 3510 m³ / s. 3 / h; the corresponding valve openings are: 15%, 17%, 20%, 27%, and 100%. This facilitates reducing frequent valve adjustments, allowing only fine-tuning. The valve control data is as follows: Figure 3 As shown.

[0083] Example 2

[0084] like Figure 2 As shown in the figure, this embodiment provides a wastewater treatment aeration control method based on multi-parameter collaborative optimization, including the following steps:

[0085] S1) Use a multi-parameter sensor module to collect multi-sensor data on COD, BOD5, DO, ammonia nitrogen, ORP, pH, influent flow rate, and sludge concentration of wastewater from the aeration tank.

[0086] S2), calculating the dynamic correction oxygen transfer coefficient according to the bubble image features; the specific calculation expression is:

[0087] K La = a A(t) + b C(t) + g V(t) + d

[0088] In the formula, K La represents the oxygen transfer coefficient; A(t) is the bubble area at time t; C is the trajectory curvature; V is the average speed of the bubble; a, b, g, and d are weight parameters of the dynamic oxygen transfer coefficient correction model.

[0089] The bubble image features are extracted from the sewage image in the aeration tank collected by the high-speed camera device through the convolutional neural network (CNN), and the extracted bubble image features include the bubble area, the trajectory curvature, and the average speed features.

[0090] S3), establishing the relationship between the influent load and aeration according to the dynamic correction oxygen transfer coefficient and the feedforward parameter; the specific calculation formula is:

[0091]

[0092] In the formula, is the blower air volume of the feedforward control at time t; K La (t) is the dynamic correction oxygen transfer coefficient; QIN(t) is the influent volume at time t; COD(t) is the oxygen demand at time t; NH3_N(t) is the ammonia nitrogen concentration at time t; k1, k2, and k3 are the feedforward control system.

[0093] S4), calculating the fine adjustment value of the blower air volume and the butterfly valve opening degree through the PID control algorithm and the feedback parameter, and correcting the aeration amount to obtain the total air volume target value; the calculation expression is:

[0094]

[0095] In the formula, is the fine adjustment value; e(t) is the DO concentration deviation at time t; wherein the DO concentration deviation is equal to the difference between the DO concentration control target target value and the actual measured value of the DO concentration; K p , K i , K d are PID control parameters of the PID controller; is the basic parameter of the PID controller; is the basic oxygen transfer coefficient.

[0096] The calculation formula of the feedback control module for correcting the aeration amount is:

[0097]

[0098] wherein, is the corrected total air volume target value at time t.

[0099] S5), adjusting the guide vane opening degree and the rotational speed of the blower according to the adaptive PID control parameters and the corrected total air volume target value by using an adaptive PID controller, and adjusting the butterfly valve opening degree according to the gradient butterfly valve adjustment strategy.

[0100] The embodiment is applied to the dissolved oxygen control of a sewage treatment plant. Compared with before the embodiment is applied, the dissolved oxygen can be stabilized at about 2 mg / L. A comparison chart of the DO concentration before and after the regulation is shown in FIG. 4. A significant stable control effect is obtained, and the energy consumption is saved by more than 15%. Figure 4

[0101] The above-described embodiments and the description in the specification are only to illustrate the principles and the best mode of the present application, and various changes and improvements can be made to the present application without departing from the spirit and the scope of the present application. Such changes and improvements are also within the scope of the present application.​

Claims

1. A wastewater treatment aeration control system based on multi-parameter collaborative optimization, characterized in that, The method comprises the following steps: A multi-parameter sensor module is arranged at the water inlet, the aeration tank and the water outlet, and is used to collect multi-sensor data of COD, BOD5, DO, ammonia nitrogen, ORP, pH, water inflow and sludge concentration of wastewater in the aeration tank; A dynamic oxygen mass transfer coefficient correction module is used to calculate a dynamically corrected oxygen mass transfer coefficient according to the bubble image features of the aeration tank; A feedforward control module is used to establish a relationship between the water inflow load and the aeration amount according to the dynamically corrected oxygen mass transfer coefficient and the feedforward parameters; A feedback control module is used to calculate a fine adjustment value of the air volume of the air blower and the opening degree of the butterfly valve through a PID control algorithm and feedback parameters, correct the aeration amount, and obtain a total air volume target value; An adaptive PID controller is used to adjust the guide vane opening degree and the rotating speed of the air blower according to the adaptive PID control parameters and the corrected total air volume target value, and adjust the opening degree of the butterfly valve according to a gradient butterfly valve adjustment strategy. The dynamic oxygen mass transfer coefficient correction module calculates the oxygen mass transfer coefficient K La The expression is: K La = a A(t) + b B(t) + c C(t) + d D(t) + e E(t) + f F(t) + g G(t) + h H(t) + i I In the formula, K La represents the oxygen mass transfer coefficient; A(t) is the bubble area at time t; C is the trajectory curvature; V is the average bubble velocity; and a, β, γ, and δ are weight parameters of the dynamic oxygen mass transfer coefficient correction model.

2. The multi-parameter coordinated optimization based aeration control system for wastewater treatment of claim 1, wherein: The feedforward parameters include the inflow COD, BOD5, ammonia nitrogen and water inflow, and the feedback parameters include the DO concentration, ORP, pH and sludge concentration.

3. The multi-parameter coordinated optimization based aeration control system for wastewater treatment of claim 1, wherein: The calculation formula of the feedforward control module for establishing the relationship between the water inflow load and the aeration according to the dynamically corrected oxygen mass transfer coefficient and the feedforward parameters is: wherein, Q(t) is the air volume of the blower at time t; K La (t) is the dynamic correction oxygen transfer coefficient; QIN(t) is the water inflow at time t; COD(t) is the oxygen demand at time t; NH3_N(t) is the ammonia nitrogen concentration at time t; k1, k2, k3 are the feedforward control system.

4. The multi-parameter coordinated optimization based aeration control system for wastewater treatment of claim 3, wherein: The calculation expression of the feedback control module for calculating the fine adjustment value of the opening degree of the butterfly valve is: In the formula, is a fine tuning value; e(t) is the DO concentration deviation at time t; K p , K i , K d PID control parameters of the PID controller; is a base parameter of the PID controller; is a base oxygen transfer coefficient.

5. The multi-parameter coordinated optimization based aeration control system for wastewater treatment of claim 4, wherein: The calculation formula of the feedback control module for correcting the aeration amount and obtaining the total air volume target value is: In the formula, is the total air volume target value after correction at time t.

6. The multi-parameter coordinated optimization based aeration control system for wastewater treatment of claim 1, wherein: The butterfly valve opening degree is divided into a low load area with an opening degree of 15%-30% and a high load area with an opening degree of 30%-100%; and the butterfly valve opening degree is divided into five levels: 15%, 17%, 20%, 27%, 30-100%; the air volume gradient corresponding to each level of opening degree is ±130 m 3 / h; the response time is set to 10 minutes.

7. The multi-parameter coordinated optimization based aeration control system for wastewater treatment of claim 6, wherein: When the DO is less than 1.5 mg / L, the up-regulation amplitude of the opening degree of the butterfly valve is 5%-10% / min; when the DO is greater than 2.5 mg / L, the down-regulation amplitude of the opening degree of the butterfly valve is 3%-8% / min.

8. A wastewater treatment aeration control method based on multi-parameter collaborative optimization, characterized by, The method utilizes the method of any one of claims 1-7 to realize aeration control, and the method comprises the following steps: S1), the multi-parameter sensor module is used to collect multi-sensor data of COD, BOD5, DO, ammonia nitrogen, ORP, pH, water inflow and sludge concentration of wastewater in the aeration tank; and the inflow COD, BOD5, ammonia nitrogen and water inflow are used as the feedforward parameters, and the DO concentration, ORP, pH and sludge concentration are used as the feedback parameters; S2), the dynamically corrected oxygen mass transfer coefficient is calculated according to the bubble image features; S3), the relationship between the water inflow load and the aeration is established according to the dynamically corrected oxygen mass transfer coefficient and the feedforward parameters; S4), the fine adjustment value of the air volume of the air blower and the opening degree of the butterfly valve is calculated through the PID control algorithm and the feedback parameters, the aeration amount is corrected, and the total air volume target value is obtained; S5), the adaptive PID controller is used to adjust the guide vane opening degree and the rotating speed of the air blower according to the adaptive PID control parameters and the corrected total air volume target value, and adjust the opening degree of the butterfly valve according to the gradient butterfly valve adjustment strategy.

9. The multi-parameter coordinated optimization based aeration control method for wastewater treatment of claim 8, wherein: In step S4), the calculation expression of the feedback control module for calculating the fine adjustment value of the opening degree of the butterfly valve is: In the formula, is a fine tuning value; e(t) is the DO concentration deviation at time t; K p , K i , K d PID control parameters of the PID controller; is a base parameter of the PID controller; is a base oxygen transfer coefficient; K La (t) represents the oxygen transfer coefficient at time t; The calculation formula of the feedback control module for correcting the aeration amount and obtaining the total air volume target value is: In the formula, is the total air volume target value after correction at time t.

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