Sewage treatment aeration control system and method based on multi-parameter collaborative optimization

Abstract

The invention provides a sewage treatment aeration control system and method based on multi-parameter collaborative optimization. The method comprises the following steps: acquiring multi-sensor data of COD (Chemical Oxygen Demand), BOD5 (Biochemical Oxygen Demand), DO (Dissolved Oxygen Demand), ammonia nitrogen, ORP (Oxidation Reduction Potential), pH (Potential of Hydrogen), water inlet flow and sludge concentration of wastewater in an aeration tank by utilizing a multi-parameter sensor module; according to the dynamic correction oxygen mass transfer coefficient and the feedforward parameter, establishing a relationship between the water inlet load and aeration; calculating the air volume of an air blower and the fine adjustment value of the opening degree of a butterfly valve through a PID control algorithm and feedback parameters, and correcting the aeration quantity to obtain a total air volume target value; and adjusting the opening degree and the rotating speed of a guide vane of the air blower according to the self-adaptive PID control parameters and the corrected total air volume target value by utilizing a self-adaptive PID controller, and adjusting the opening degree of a butterfly valve according to a gradient butterfly valve adjusting strategy. The output of the air blower is optimized by adopting self-adaptive PID, and the opening degree of the butterfly valve of each biological tank is dynamically adjusted through a gradient valve adjusting strategy, so that the accurate control of the aeration rate is realized; the sewage treatment effect is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to a sewage treatment aeration control system and method based on multi-parameter collaborative optimization. Background Technique

[0002] Sewage treatment is an important link in environmental protection. Among them, the aeration process is the core step of the activated sludge process, which directly affects the sewage treatment effect and energy consumption. Traditional aeration control technologies mainly rely on single-parameter feedback (such as dissolved oxygen) or simple feedforward models, and usually adopt linear adjustment methods, that is, the opening degree of the butterfly valve is linearly related to the air volume. However, this adjustment method has insufficient control accuracy in high-load areas and low-load areas and is difficult to meet the requirements under complex working conditions. Secondly, the following problems exist:

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

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

[0005] 3. Poor adaptability to multiple processes: Existing systems are difficult to be compatible with different working conditions such as A 2 / O and oxidation ditches.

[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] Aiming at the deficiencies of the prior art, the present invention provides a sewage treatment aeration control system and method based on multi-parameter collaborative optimization. The present invention significantly improves the DO control accuracy through multi-parameter collaborative optimization and dynamic oxygen mass transfer coefficient correction, and dynamically adjusts the opening degree of the butterfly valve of each biological tank through a gradient valve adjustment strategy to achieve precise control of the aeration volume and ensure the sewage treatment effect.

[0008] The technical solution of the present invention is: A sewage treatment aeration control system based on multi-parameter collaborative optimization, including:

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

[0010] A dynamic oxygen mass transfer coefficient correction module, which is used to calculate the dynamically corrected oxygen mass transfer coefficient according to the bubble image characteristics of the aeration tank;

[0011] A feedforward control module, which establishes the relationship between the influent load and aeration according to the dynamically corrected oxygen mass transfer coefficient and feedforward parameters;

[0012] A feedback control module, which is used to calculate the fine-tuning values of the blower air volume and butterfly valve opening degree through the PID control algorithm and feedback parameters, and correct the aeration volume to obtain the total air volume target value;

[0013] An adaptive PID controller, which is used to adjust the opening degree and speed of the blower guide vane 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.

[0014] Preferably, after the multi-parameter sensor module collects multi-sensor data, it performs time alignment and normalization processing on the data, and then uses the Kalman filter or weighted fusion algorithm for fusion.

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

[0016] Preferably, the dynamic oxygen mass transfer coefficient correction module calculates the oxygen mass transfer coefficient K La The expression is:

[0017] K La =α·A(t)+β·C(t)+γ·V(t)+δ

[0018] 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; α, β, γ, δ are the weight parameters of the dynamic oxygen mass transfer coefficient correction model.

[0019] Preferably, the bubble image characteristics 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 characteristics include bubble area, trajectory curvature, and average velocity characteristics.

[0020] Preferably, the calculation formula for the feedforward control module to establish the relationship between the influent load and aeration according to the dynamically corrected oxygen mass transfer coefficient and feedforward parameters is:

[0021]

[0022] In the formula, is the blower air volume of the feedforward control at time t; K La(t) is the dynamically corrected oxygen mass transfer coefficient; QIN(t) is the influent flow rate 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 systems.

[0023] Preferably, the calculation expression for the feedback control module to calculate the fine-tuning value is:

[0024]

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

[0026] Preferably, the calculation formula for the feedback control module to correct the aeration volume to obtain the total air volume target value is:

[0027]

[0028] In the formula, is the total air volume target value corrected at time t.

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

[0030] Preferably, when DO < 1.5 mg / L, the upward adjustment amplitude of the butterfly valve opening is 5%-10% / min; when DO > 2.5 mg / L, the downward adjustment amplitude of the butterfly valve opening is 3%-8% / min.

[0031] Preferably, the present invention also provides a sewage treatment aeration control method based on multi-parameter collaborative optimization, including the following steps:

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

[0033] S2), calculating the dynamically corrected oxygen mass transfer coefficient according to the bubble image characteristics;

[0034] S3), establishing the relationship between the influent load and aeration according to the dynamically corrected oxygen mass transfer coefficient and the feedforward parameters;

[0035] S4), calculate the fine-tuning values of the air volume of the blower and the opening degree of the butterfly valve through the PID control algorithm and the feedback parameters, and correct the aeration volume to obtain the total air volume target value;

[0036] S5), use the adaptive PID controller to adjust the guide vane opening degree and rotation speed of the 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.

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

[0038] 1. The present invention uses adaptive PID to optimize the output of the blower, and dynamically adjusts the opening degree of the butterfly valve in each biological pond through the gradient valve adjustment strategy to achieve precise control of the aeration volume;

[0039] 2. The present invention dynamically adjusts the aeration, thereby controlling the opening degree of the butterfly valve and the air volume of the blower, and improving the aeration efficiency;

[0040] 3. The present invention significantly improves the DO control accuracy through multi-parameter collaborative optimization and dynamic oxygen mass transfer coefficient correction, and can be stably controlled at the level of 1.5 - 2 mg / L to ensure the sewage treatment effect;

[0041] 4. The present invention reduces the energy consumption of the blower by 10% - 30% through precise prediction and optimization of the aeration volume. Description of the Drawings

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

[0043] Figure 2 is the flow schematic diagram of the method in Embodiment 2 of the present invention;

[0044] Figure 3 The curve graph of valve regulation in Embodiment 1 of the present invention;

[0045] Figure 4 The comparison graph of DO concentration before and after implementation in Embodiment 1 of the present invention. Detailed Embodiments

[0046] The following further describes the detailed embodiments of the present invention with reference to the drawings:

[0047] Embodiment 1

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

[0049] A multi-parameter sensor module is arranged at the water inlet, aeration tank and water outlet for collecting multi-sensor data of COD, BOD5, DO, ammonia nitrogen, ORP, pH, influent flow rate, and sludge concentration of the wastewater;

[0050] A dynamic oxygen mass transfer coefficient correction module is used to calculate the dynamically corrected oxygen mass transfer coefficient according to the bubble image characteristics of the aeration tank;

[0051] A feedforward control module establishes the relationship between the influent load and aeration based on the dynamically corrected oxygen mass transfer coefficient and feedforward parameters;

[0052] A feedback control module is used to calculate the fine-tuning values of the blower air volume and butterfly valve opening through the PID control algorithm and feedback parameters, and correct the aeration volume to obtain the total air volume target value;

[0053] An adaptive PID controller is used to adjust the opening and rotation speed of the blower guide vane according to the adaptive PID control parameters and the corrected total air volume target value, and adjust the butterfly valve opening according to the gradient butterfly valve adjustment strategy.

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

[0055] And in this embodiment, COD, BOD5, ammonia nitrogen, and influent flow are used as feedforward parameters; DO concentration, ORP, pH, sludge concentration, and effluent ammonia nitrogen are used as feedback parameters.

[0056] Preferably in this embodiment, after the multi-parameter sensor module collects multi-sensor data, time alignment and normalization processing are performed on it. Among them, the DO concentration is also fused using the Kalman filtering algorithm, specifically as follows:

[0057] 1) Time alignment

[0058] For the time synchronization of multi-sensor data in time alignment, the linear interpolation method is used to align non-uniformly sampled data. Assume that the measured values of sensor A at time points t1 and t2 are X A (t1) and X A (t2), which need to be aligned to the time stamp t of sensor B (t1 < t < t2):

[0059]

[0060] 2) Normalization processing

[0061] The Z-score normalization is used to eliminate the dimension difference, and the formula is:

[0062]

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

[0064] 3) Kalman filter algorithm fusion

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

[0066]

[0067] In the formula; x i is the measured 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] Preferably in this embodiment, the dynamic oxygen mass transfer coefficient correction module calculates the oxygen mass transfer coefficient K la The expression is:

[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 bubble velocity; α, β, γ, δ are the weight parameters of the dynamic oxygen mass transfer coefficient correction model.

[0071] Among them, 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. The extracted bubble image features include bubble area, trajectory curvature and average velocity features.

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

[0073]

[0074] In the formula, is the air volume of the blower for feedforward control at time t; K La (t) is the dynamically corrected oxygen mass transfer coefficient; QIN(t) is the influent flow 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 systems.

[0075] Preferably in this embodiment, the calculation expression for the feedback control module to calculate the fine-tuning value 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 are the PID control parameters of the PID controller; are the basic parameters of the PID controller; is the basic oxygen mass transfer coefficient.

[0078] The calculation formula for the feedback control module to correct the aeration volume is:

[0079]

[0080] In the formula, is the target value of the total air volume corrected at time t.

[0081] Preferably in this embodiment, the valve opening is finely adjusted within a small range, specifically the low load area with an opening of 15%-30% and the high load area with an opening of 30%-100%; in this embodiment, the butterfly valve opening is divided into 5 levels: the lower limits are 15%, 17%, 20%, 27%, 30-100%; the air volume gradient corresponding to each level of opening is ±130 m 3 / h; as Figure 3 shown, the response time is set to 10 minutes. The effective adjustment range is within 30%, and the adjustment ability is lost for 30-100%. When DO < 1.5 mg / L, the upward adjustment range of the butterfly valve opening is 5%-10% / min; when DO > 2.5 mg / L, the downward adjustment range of the butterfly valve opening is 3%-8% / min.

[0082] To achieve the adjustment of the valve according to the equal-gradient air volume change each time, the opening adjustment process of the valve is as follows: the upper limit of the valve opening is 100%, and the lower limit is 15%. The air volume change gradient is generally set to 5, and here it is set to 5 air volume change gradients, which are: 2980, 3110, 3240, 3370, 3510 m 3 / h; the corresponding valve openings are: 15%, 17%, 20%, 27%, 100%. To reduce the frequent adjustment of the valve, only fine adjustment is made, and the valve control data is as Figure 3 shown.

[0083] Embodiment 2

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

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

[0086] S2), calculate the dynamically corrected oxygen mass transfer coefficient according to the bubble image characteristics; the specific calculation expression is:

[0087] K La = α·A(t)+β·C(t)+γ·V(t)+δ

[0088] 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; α, β, γ, δ are the weight parameters of the dynamic oxygen mass transfer coefficient correction model.

[0089] Among them, the bubble image characteristics are extracted from the sewage image in the aeration tank collected by the high-speed camera through the convolutional neural network CNN, and the extracted bubble image characteristics include bubble area, trajectory curvature and average velocity characteristics.

[0090] S3), establish the relationship between the influent load and aeration according to the dynamically corrected oxygen mass transfer coefficient and the feedforward parameters; the specific calculation formula is:

[0091]

[0092] In the formula, is the air volume of the blower for feedforward control at time t; K La (t) is the dynamically corrected oxygen mass transfer coefficient; QIN(t) is the influent flow rate 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 systems.

[0093] S4), calculate the fine-tuning values of the blower air volume and the butterfly valve opening through the PID control algorithm and the feedback parameters, and correct the aeration volume to obtain the total air volume target value; its calculation expression is:

[0094]

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

[0096] The calculation formula for the feedback control module to correct the aeration volume is:

[0097]

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

[0099] S5), using the adaptive PID controller, adjust the opening degree and speed of the blower guide vane 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.

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

[0101] The above embodiments and the descriptions in the specification only illustrate the principles and the best embodiments of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. A sewage treatment aeration control system based on multi-parameter collaborative optimization, characterized in that: include: The multi-parameter sensor module is installed at the water inlet, aeration tank and outlet to collect multi-sensor data of COD, BOD5, DO, ammonia nitrogen, ORP, pH, inlet flow and sludge concentration of aeration tank wastewater; A dynamic oxygen mass transfer coefficient correction module is used to calculate the dynamic corrected oxygen mass transfer coefficient according to the bubble image characteristics of the aeration tank; A feedforward control module establishes the relationship between the influent load and the aeration volume based on the dynamically modified oxygen mass transfer coefficient and the feedforward parameters; The feedback control module is used to calculate the fine-tuning value of the blower air volume and the butterfly valve opening through the PID control algorithm and feedback parameters, and to correct the aeration volume to obtain the total air volume target value; The adaptive PID controller is used to adjust the blower guide vane opening and speed according to the adaptive PID control parameters and the modified total air volume target value, and adjust the butterfly valve opening according to the gradient butterfly valve adjustment strategy.

2. The sewage treatment aeration control system based on multi-parameter collaborative optimization according to claim 1 is characterized in that: The feedforward parameters include influent COD, BOD5, ammonia nitrogen, and influent flow; the feedback parameters include DO concentration, ORP, pH, sludge concentration, and effluent ammonia nitrogen.

3. The sewage treatment aeration control system based on multi-parameter collaborative optimization according to claim 1, characterized in that: The dynamic oxygen mass transfer coefficient correction module calculates the oxygen mass transfer coefficient K La The expression is: K La =α·A(t)+β·C(t)+γ·V(t)+δ 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; α, β, γ, and δ are the weight parameters of the dynamic oxygen mass transfer coefficient correction model.

4. A sewage treatment aeration control system based on multi-parameter collaborative optimization according to claim 3, characterized in that: The feedforward control module establishes the relationship between the influent load and aeration according to the dynamic modified oxygen mass transfer coefficient and the feedforward parameter as follows: In the formula, is the blower air volume of feedforward control at time t; K La (t) is the dynamically corrected oxygen mass transfer coefficient; QIN(t) is the water inlet 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 feedforward control systems.

5. A sewage treatment aeration control system based on multi-parameter collaborative optimization according to claim 4, characterized in that: The calculation expression of the fine-tuning value of the butterfly valve opening calculated by the feedback control module is: In the formula, is the fine-tuning value; e(t) is the DO concentration deviation at time t; K p , K i , K d is the PID control parameter of the PID controller; is the basic parameter of PID controller; is the basic oxygen mass transfer coefficient.

6. A sewage treatment aeration control system based on multi-parameter collaborative optimization according to claim 5, characterized in that: The feedback control module corrects the aeration volume and obtains the total air volume target value by the following calculation formula: In the formula, It is the corrected total air volume target value at time t.

7. The sewage treatment aeration control system based on multi-parameter collaborative optimization according to claim 1 is characterized in that: The butterfly valve opening is divided into a low load area with an opening of 15%-30% and a high load area with an opening of 30%-100%; and the butterfly valve opening is divided into five levels: 15%, 17%, 20%, 27%, 30-100%; each level of opening corresponds to an air volume gradient of ±130m 3 / h; the response time is set to 10 minutes.

8. The sewage treatment aeration control system based on multi-parameter collaborative optimization according to claim 7, characterized in that: When DO<1.5mg / L, the butterfly valve opening is adjusted upward by 5%-10% / min; when DO>2.5mg / L, the butterfly valve opening is adjusted downward by 3%-8% / min.

9. A sewage treatment aeration control method based on multi-parameter collaborative optimization, characterized in that: The method uses the method described in any one of claims 1 to 8 to achieve aeration control, and the method comprises the following steps: S1), using a multi-parameter sensor module to collect multi-sensor data of COD, BOD5, DO, ammonia nitrogen, ORP, pH, inlet flow rate, and sludge concentration of aeration tank wastewater; and using COD, BOD5, ammonia nitrogen, and inlet flow rate as feedforward parameters; and using DO concentration, ORP, pH, sludge concentration, and effluent ammonia nitrogen as feedback parameters; S2), calculating the dynamically corrected oxygen mass transfer coefficient according to the bubble image characteristics; S3), establishing the relationship between the influent load and aeration based on the dynamically corrected oxygen mass transfer coefficient and the feedforward parameter; S4), calculating the fine-tuning value of the blower air volume and the butterfly valve opening through the PID control algorithm and feedback parameters, and correcting the aeration volume to obtain the total air volume target value; S5), using an adaptive PID controller to adjust the blower guide vane opening and speed according to the adaptive PID control parameters and the corrected total air volume target value, and adjusting the butterfly valve opening according to the gradient butterfly valve adjustment strategy.

10. The sewage treatment aeration control method based on multi-parameter collaborative optimization according to claim 9, characterized in that: In step S4), the feedback control module calculates the fine-tuning value of the butterfly valve opening using the following calculation expression: In the formula, is the fine-tuning value; e(t) is the DO concentration deviation at time t; K p , K i , K d is the PID control parameter of the PID controller; is the basic parameter of PID controller; is the basic oxygen mass transfer coefficient; K La (t) represents the oxygen mass transfer coefficient at time t; The feedback control module corrects the aeration volume and obtains the total air volume target value by the following calculation formula: In the formula, It is the corrected total air volume target value at time t.

Images