Knowledge guidance-based sewage treatment aeration process collaborative optimization control method

Abstract

According to the sewage treatment aeration process collaborative optimization control method based on knowledge guidance, optimization control over dissolved oxygen of different galleries is achieved, and the purposes of improving the effluent quality and reducing aeration energy consumption are achieved. According to the method, a sewage treatment aeration process collaborative optimization control model based on mechanism knowledge is established, and the relation between different gallery dissolved oxygen concentrations and effluent quality and aeration energy consumption and the relation between the different gallery dissolved oxygen concentrations are obtained; a collaborative optimization method based on evolutionary knowledge is designed, and a dissolved oxygen concentration set value capable of effectively reducing aeration energy consumption and improving effluent quality is obtained; and designing a multivariable proportional-integral-differential controller to realize optimal set value tracking control.

Description

Technical Field

[0001] Aiming at the optimization operation problem of sewage treatment aeration process, the present invention designs a knowledge-guided sewage treatment aeration process collaborative optimization control method, establishes a sewage treatment aeration process collaborative optimization control model based on mechanism knowledge, designs a collaborative optimization method based on evolutionary knowledge to obtain the optimized set value, and designs a multivariable proportional-integral-differential controller to track and control the optimized set value. While ensuring the effluent quality, reducing aeration energy consumption is of great significance to the efficient and stable operation of the sewage treatment process. The invention belongs to both the field of water research and the field of intelligent optimization control. Background Art

[0002] With the accelerated progress of industrialization and urbanization in my country, the amount of domestic sewage discharged has increased year by year, which has put forward new requirements for sewage treatment capacity, making it face the dual challenges of meeting emission standards and energy conservation and consumption reduction. The aeration process of sewage treatment oxidizes ammonia nitrogen into nitrate nitrogen through continuous aeration, thereby reducing the concentration of ammonia nitrogen in the effluent. However, the aeration energy consumption accounts for more than half of the operating energy consumption of the sewage treatment plant, becoming a vital part of the sewage treatment process. In order to improve the operating effect and efficiency, optimization control strategies have been widely used in the aeration process of sewage treatment.

[0003] The goal of optimizing the aeration process in sewage treatment is to ensure that the effluent quality meets the standard and reduce the aeration energy consumption. However, the operating mechanism of the aeration process in sewage treatment is complex, there is a strong conflict between the effluent quality and the aeration energy consumption, and the dissolved oxygen in multiple galleries affects each other, which is difficult to solve. Therefore, how to solve the optimal set value of the aeration process in sewage treatment to achieve the effluent quality and reduce the aeration energy consumption is an important research topic. In the process of establishing the effluent water quality and aeration energy consumption model, it is difficult to accurately express the optimization model using a mechanism model because the dynamics of the sewage treatment process is complex and nonlinear and the dissolved oxygen in multiple galleries affects each other. Therefore, a modeling method based on data and knowledge is used to accurately describe the optimization objectives and constraints of the sewage treatment process. In addition, multiple objectives conflict with each other and the constraints are complex in the sewage treatment process, which affects the quality of solving the optimization set point and easily leads to poor performance of the optimization control. Therefore, designing an optimization control method based on knowledge and data can not only improve the quality of solving the optimization set point, thereby ensuring the effluent quality while reducing energy consumption, but also ensure the stable and efficient operation of the aeration process in sewage treatment.

[0004] By analyzing the characteristics of the aeration process of sewage treatment, the present invention established operating performance indicators and constraints including effluent water quality and aeration energy consumption, and designed a collaborative optimization method based on evolutionary knowledge, which effectively reduced aeration energy consumption and increased the set value of dissolved oxygen concentration of effluent water quality, thereby achieving efficient and stable operation of the aeration process of sewage treatment. Summary of the invention

[0005] The present invention proposes a knowledge-guided collaborative optimization control method for aeration process in sewage treatment, which reduces aeration energy consumption while ensuring effluent quality. This method establishes a collaborative optimization control model for aeration process in sewage treatment based on mechanism knowledge, designs a collaborative optimization method based on evolutionary knowledge, and uses a multivariable proportional-integral-differential controller to control process variables to track the optimized set values ​​in real time.

[0006] The present invention adopts the following technical solutions and implementation steps:

[0007] 1. A knowledge-guided collaborative optimization control method for aeration process in sewage treatment, comprising the following steps:

[0008] (1) Establish a collaborative optimization control model for the sewage treatment aeration process based on mechanism knowledge

[0009] Considering the dissolved oxygen concentrations in multiple corridors as decision variables, the aeration energy consumption and effluent quality of the sewage treatment aeration process are taken as optimization control targets:

[0010]

[0011] Among them, J EQ (t) is the effluent quality model of the sewage treatment aeration process at time t, J AE (t) is the aeration energy consumption model of the sewage treatment aeration process at time t, z EQ (t) = [S O,3 (t),S O,4 (t),S O,5 (t),S NO (t),S NH (t),SS(t)],z AE (t) = [S O,3 (t),S O,4 (t),S O,5 (t),S NO (t),MLSS(t)],S O,3 (t) is the dissolved oxygen concentration in the third corridor at time t, S O,4 (t) is the dissolved oxygen concentration in the fourth corridor at time t, S O,5 (t) is the dissolved oxygen concentration in the fifth corridor at time t, S O,3 (t), S O,4 (t) and S O,5 (t) is the decision variable, S NO (t) is the nitrate nitrogen concentration at time t, S NH (t) is the ammonia nitrogen concentration at time t, SS(t) is the suspended solids concentration at time t, MLSS(t) is the mixed suspended solids concentration at time t, W EQ,h (t) and W AE,h(t) is the connection weight of the hth kernel function of effluent quality and aeration energy consumption at time t, and is the central value of the hth kernel function of effluent quality and aeration energy consumption at time t, b EQ,h (t) and b AE,h (t) is the width value of the hth kernel function of effluent water quality and aeration energy consumption at time t;

[0012] Mechanistic knowledge of the wastewater treatment aeration process and feasible ranges of variables as optimization control constraints:

[0013]

[0014] Where x(t) = [S O,3 (t),S O,4 (t),S O,5 (t)] is the decision variable vector at time t, S O,4 (t-1) is the dissolved oxygen concentration in the fourth corridor at time t-1, S O,5 (t-1) is the dissolved oxygen concentration in the fifth corridor at time t-1, a4, b4, c4 are related to S O,4 (t) related least squares regression coefficients, a5, b5, c5 are related to S O,5 (t), g1(x(t)) and g2(x(t)) are the constraints at time t established based on the mechanism knowledge, and the material balance equation of dissolved oxygen in multiple corridors is used as the mechanism knowledge:

[0015]

[0016] Among them, S * O is the dissolved oxygen saturation concentration, V k is the volume of the kth aerobic corridor, r k is the reaction rate of the kth aerobic corridor, Q k is the flow rate of the kth aerobic corridor, (K L a) k is the oxygen transfer coefficient of the kth aerobic corridor, k = 4,5, S O,k-1 and S O,k The nonlinear relationship is expressed as:

[0017] S O,k (t) = a k S O,k-1 (t)+b k S O,k (t-1)+c k (5)

[0018] Among them, a k ,b k,c k is the least squares regression coefficient associated with the dissolved oxygen concentration in the kth corridor:

[0019]

[0020] Where, τ=1,2,…,R, R is the total number of sample data;

[0021] (2) Designing collaborative optimization methods based on evolutionary knowledge

[0022] Set the total number of iterations to solve the optimization setpoint to κ max =500, the particle swarm size is Λ=50, x t,n (κ)∈X Γ is the position vector of the nth particle evolving to the κth generation at time t, X Γ is a Γ-dimensional search space;

[0023] Convert an equality constraint to an inequality constraint:

[0024]

[0025] Among them, h1(x(t)) is the inequality constraint after g1(x(t)) is transformed, h2(x(t)) is the inequality constraint after g2(x(t)) is transformed, ε1 and ε2 are the fault tolerance parameters of g1(x(t)) and g2(x(t)):

[0026] ε1=(1-p1(κ))×max(g1(x(t))) (8)

[0027] ε2=(1-p2(κ))×max(g2(x(t))) (9)

[0028] Among them, p1(κ) is the ratio of the number of feasible solutions satisfying g1(x(t)) in the κth generation to the population size in the κth generation, p2(κ) is the ratio of the number of feasible solutions satisfying g2(x(t)) in the κth generation to the population size in the κth generation, and the constraint violation degree is The feasible domain is

[0029] if Update the particle's velocity:

[0030] v t,n (κ+1)=0.7v t,n (κ)+0.5μ1(pBest t,n (κ)-x t,n (κ))+0.5μ2(gBest t (κ)-x t,n (κ)) (10)

[0031] Among them, v t,n (κ+1) is the speed vector of the nth particle evolving to the κ+1th generation at time t, μ1 is the individual evolution random number and its value range is [0,1], μ2 is the population evolution random number and its value range is [0,1], pBest t,n (κ) is the best position of the nth particle evolving to the κth generation at time t, gBest t (κ) is the global best position of the κth generation at time t. The non-dominated solution of the κth generation at time t is stored in the external archive, and gBest is selected from the external archive. t (κ);

[0032] if Change the fitness value of the particle to:

[0033]

[0034] in, is J(x t,n (κ)) The fitness value after the change, J(x t,n (κ))=[J EQ (x t,n (κ)),J AE (x t,n (κ))], I = [1,1] T , update the particle speed as:

[0035]

[0036] Among them, μ3 is a knowledge evolution random number with a value range of [0,1], p(κ) is the ratio of the number of feasible solutions in the κth generation to the size of the population in the κth generation, K tn (κ) is the knowledge acquired by evolving to the κth generation at time t:

[0037]

[0038] Among them, r is the number of generations of historical evolution recorded, 0 <r<5, and x t,n (κ-r) satisfies:

[0039]

[0040] Among them, d n,i (κ-r) is x t,n (κ-r) and x t,n′ Euclidean distance of (κ-r);

[0041] The particle's position is updated as:

[0042] xt,n (κ+1)=x t,n (κ)+v t,n (κ+1) (15)

[0043] Among them, x t,n (κ+1) is the position of particle n when it evolves to the κ+1th generation at time t, x t,i (κ+1) is the position of particle i at time t when it evolves to the κ+1th generation; check whether the evolution process reaches the stopping condition: if the evolutionary generation κ<κ max , the evolutionary number κ increases by 1 and the particle's speed and position are updated again; if the evolutionary number κ = κ max , terminate the evolution process, from the κth max A solution is randomly selected from the solutions of the previous generation as the optimal setting value y of the process variable * (t) = [S * O,3 (t),S * O,4 (t),S * O,5 (t)],S * O,3 (t) is the optimal set value of dissolved oxygen in the third corridor at time t, S * O,4 (t) is the optimal set value of dissolved oxygen in the fourth corridor at time t, S * O,5 (t) is the optimal set value of dissolved oxygen in the fifth corridor at time t;

[0044] (3) Design of multivariable proportional-integral-derivative controller

[0045] Using a multivariable proportional-integral-derivative controller to optimize the setpoint y * (t) Conduct tracking control:

[0046]

[0047] Where, Δu(t)=[ΔK L a3(t),ΔK L a4(t),ΔK L a5(t)] T is the operating variable matrix at time t, ΔK L a3(t) is the change in the dissolved oxygen transfer coefficient of the third corridor at time t, ΔK L a4(t) is the change in the dissolved oxygen transfer coefficient of the fourth corridor at time t, ΔK La5(t) is the change in the dissolved oxygen transfer coefficient of the fifth corridor at time t, C = [200, 200, 200] is the proportional coefficient, L = [15, 15, 15] is the integral time constant, F = [2, 2, 2] is the differential time constant, e(t) = y(t)-y * (t) is the control error vector at time t, y(t) = [S O,3 (t),S O,4 (t),S O,5 (t)] is the actual output at time t;

[0048] Use Δu(t) to adjust the dissolved oxygen transfer coefficient and internal recirculation rate:

[0049]

[0050] Among them, K L a3(t+1) is the dissolved oxygen transfer coefficient of the third corridor at time t+1, K L a3(t) is the dissolved oxygen transfer coefficient of the third corridor at time t, K L a4(t+1) is the dissolved oxygen transfer coefficient of the fourth corridor at time t+1, K L a4(t) is the dissolved oxygen transfer coefficient of the fourth corridor at time t, K L a5(t+1) is the dissolved oxygen transfer coefficient of the fifth corridor at time t+1, K L a5(t) is the dissolved oxygen transfer coefficient of the fifth corridor at time t;

[0051] The input of the sewage treatment aeration process optimization control system at time t is Δu(t), and by operating ΔK L a3(t) realizes S O,3 (t) tracking control, by operating ΔK L a4(t) realizes S O,4 (t) tracking control, by operating ΔK L a5(t) realizes S O,5 (t) tracking control, the dissolved oxygen concentration in the third corridor is adjusted to S * O,3 (t), the dissolved oxygen concentration in the fourth corridor was adjusted to S * O,4 (t), the dissolved oxygen concentration in the fifth corridor was adjusted to S * O,5 (t). BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is the dissolved oxygen concentration S in the third corridor of the present invention O,3 Optimize control effect diagram and error diagram;

[0053] Figure 2 is the dissolved oxygen concentration S in the fourth corridor of the present invention O,4 Optimization control effect diagram and error diagram.

[0054] Figure 3 is the dissolved oxygen concentration S in the fifth corridor of the present invention O,5 Optimization control effect diagram and error diagram. DETAILED DESCRIPTION

[0055] 1. A knowledge-guided collaborative optimization control method for aeration process in sewage treatment, comprising the following steps:

[0056] (1) Establish a collaborative optimization control model for the sewage treatment aeration process based on mechanism knowledge

[0057] Considering the dissolved oxygen concentrations in multiple corridors as decision variables, the aeration energy consumption and effluent quality of the sewage treatment aeration process are taken as optimization control targets:

[0058]

[0059] Among them, J EQ (t) is the effluent quality model of the sewage treatment aeration process at time t, J AE (t) is the aeration energy consumption model of the sewage treatment aeration process at time t, z EQ (t) = [S O,3 (t),S O,4 (t),S O,5 (t),S NO (t),S NH (t),SS(t)],z AE (t) = [S O,3 (t),S O,4 (t),S O,5 (t),S NO (t),MLSS(t)],S O,3 (t) is the dissolved oxygen concentration in the third corridor at time t, S O,4 (t) is the dissolved oxygen concentration in the fourth corridor at time t, S O,5 (t) is the dissolved oxygen concentration in the fifth corridor at time t, S O,3 (t), S O,4 (t) and S O,5 (t) is the decision variable, S NO (t) is the nitrate nitrogen concentration at time t, S NH (t) is the ammonia nitrogen concentration at time t, SS(t) is the suspended solids concentration at time t, MLSS(t) is the mixed suspended solids concentration at time t, W EQ,h (t) and W AE,h (t) is the connection weight of the hth kernel function of effluent quality and aeration energy consumption at time t, and is the central value of the hth kernel function of effluent quality and aeration energy consumption at time t, b EQ,h (t) and b AE,h (t) is the width value of the hth kernel function of effluent water quality and aeration energy consumption at time t;

[0060] Mechanistic knowledge of the wastewater treatment aeration process and feasible ranges of variables as optimization control constraints:

[0061]

[0062] Where x(t) = [S O,3 (t),S O,4 (t),S O,5 (t)] is the decision variable vector at time t, S O,4 (t-1) is the dissolved oxygen concentration in the fourth corridor at time t-1, S O,5 (t-1) is the dissolved oxygen concentration in the fifth corridor at time t-1, a4, b4, c4 are related to S O,4 (t) related least squares regression coefficients, a5, b5, c5 are related to S O,5 (t), g1(x(t)) and g2(x(t)) are the constraints at time t established based on the mechanism knowledge, and the material balance equation of dissolved oxygen in multiple corridors is used as the mechanism knowledge:

[0063]

[0064] Among them, S * O is the dissolved oxygen saturation concentration, V k is the volume of the kth aerobic corridor, r k is the reaction rate of the kth aerobic corridor, Q k is the flow rate of the kth aerobic corridor, (K L a) k is the oxygen transfer coefficient of the kth aerobic corridor, k = 4,5, S O,k-1 and S O,k The nonlinear relationship is expressed as:

[0065] S O,k (t) = a k S O,k-1 (t)+b k S O,k (t-1)+c k (twenty two)

[0066] Among them, a k ,b k ,c kis the least squares regression coefficient associated with the dissolved oxygen concentration in the kth corridor:

[0067]

[0068] Where, τ=1,2,…,R, R is the total number of sample data;

[0069] (2) Designing collaborative optimization methods based on evolutionary knowledge

[0070] Set the total number of iterations to solve the optimization setpoint to κ max =500, the particle swarm size is Λ=50, x t,n (κ)∈X Γ is the position vector of the nth particle evolving to the κth generation at time t, X Γ is a Γ-dimensional search space;

[0071] Convert an equality constraint to an inequality constraint:

[0072]

[0073] Among them, h1(x(t)) is the inequality constraint after g1(x(t)) is transformed, h2(x(t)) is the inequality constraint after g2(x(t)) is transformed, ε1 and ε2 are the fault tolerance parameters of g1(x(t)) and g2(x(t)):

[0074]

[0075] Among them, p1(κ) is the ratio of the number of feasible solutions satisfying g1(x(t)) in the κth generation to the population size in the κth generation, and p2(κ) is the number of feasible solutions satisfying g1(x(t)) in the κth generation.

[0076] The ratio of the number of feasible solutions to g2(x(t)) to the size of the population in the κth generation, and the constraint violation degree is The feasible domain is

[0077] if Update the particle's velocity:

[0078] v t,n (κ+1)=0.7v t,n (κ)+0.5μ1(pBest t,n (κ)-x t,n (κ))+0.5μ2(gBest t (κ)-x t,n (κ)) (27)

[0079] Among them, v t,n(κ+1) is the speed vector of the nth particle evolving to the κ+1th generation at time t, μ1 is the individual evolution random number and its value range is [0,1], μ2 is the population evolution random number and its value range is [0,1], pBest t,n (κ) is the best position of the nth particle evolving to the κth generation at time t, gBest t (κ) is the global best position of the κth generation at time t. The non-dominated solution of the κth generation at time t is stored in the external archive, and gBest is selected from the external archive. t (κ);

[0080] if Change the fitness value of the particle to:

[0081]

[0082] in, is J(x t,n (κ)) The fitness value after the change, J(x t,n (κ))=[J EQ (x t,n (κ)),J AE (x t,n (κ))], I = [1,1] T , update the particle speed as:

[0083]

[0084] Among them, μ3 is a knowledge evolution random number with a value range of [0,1], p(κ) is the ratio of the number of feasible solutions in the κth generation to the size of the population in the κth generation, K t,n (κ) is the knowledge acquired by evolving to the κth generation at time t:

[0085]

[0086] Among them, r is the number of generations of historical evolution recorded, 0 <r<5, and x t,n (κ-r) satisfies:

[0087]

[0088] Among them, d n,i (κ-r) is x t,n (κ-r) and x t,n′ Euclidean distance of (κ-r);

[0089] The particle's position is updated as:

[0090] x t,n (κ+1)=x t,n (κ)+vt,n (κ+1) (32)

[0091] Among them, x t,n (κ+1) is the position of particle n when it evolves to the κ+1th generation at time t, x t,i (κ+1) is the position of particle i at time t when it evolves to the κ+1th generation; check whether the evolution process reaches the stopping condition: if the evolutionary generation κ<κ max , the evolutionary number κ increases by 1 and the particle's speed and position are updated again; if the evolutionary number κ = κ max , terminate the evolution process, from the κth max A solution is randomly selected from the solutions of the previous generation as the optimal setting value y of the process variable * (t) = [S * O,3 (t),S * O,4 (t),S * O,5 (t)],S * O,3 (t) is the optimal set value of dissolved oxygen in the third corridor at time t, S * O,4 (t) is the optimal set value of dissolved oxygen in the fourth corridor at time t, S * O,5 (t) is the optimal set value of dissolved oxygen in the fifth corridor at time t;

[0092] (3) Design of multivariable proportional-integral-derivative controller

[0093] Using a multivariable proportional-integral-derivative controller to optimize the setpoint y * (t) Conduct tracking control:

[0094]

[0095] Where, Δu(t)=[ΔK L a3(t),ΔK L a4(t),ΔK L a5(t)] T is the operating variable matrix at time t, ΔK L a3(t) is the change in the dissolved oxygen transfer coefficient of the third corridor at time t, ΔK L a4(t) is the change in the dissolved oxygen transfer coefficient of the fourth corridor at time t, ΔK L a5(t) is the change in the dissolved oxygen transfer coefficient of the fifth corridor at time t, C = [200, 200, 200] is the proportional coefficient, L = [15, 15, 15] is the integral time constant, F = [2, 2, 2] is the differential time constant, e(t) = y(t)-y *(t) is the control error vector at time t, y(t) = [S O,3 (t),S O,4 (t),S O,5 (t)] is the actual output at time t;

[0096] Use Δu(t) to adjust the dissolved oxygen transfer coefficient and internal recirculation rate:

[0097]

[0098] Among them, K L a3(t+1) is the dissolved oxygen transfer coefficient of the third corridor at time t+1, K L a3(t) is the dissolved oxygen transfer coefficient of the third corridor at time t, K L a4(t+1) is the dissolved oxygen transfer coefficient of the fourth corridor at time t+1, K L a4(t) is the dissolved oxygen transfer coefficient of the fourth corridor at time t, K L a5(t+1) is the dissolved oxygen transfer coefficient of the fifth corridor at time t+1, K L a5(t) is the dissolved oxygen transfer coefficient of the fifth corridor at time t;

[0099] The input of the sewage treatment aeration process optimization control system at time t is Δu(t), and by operating ΔK L a3(t) realizes S O,3 (t) tracking control, by operating ΔK L a4(t) realizes S O,4 (t) tracking control, by operating ΔK L a5(t) realizes S O,5 (t) tracking control, the dissolved oxygen concentration in the third corridor is adjusted to S * O,3 (t), the dissolved oxygen concentration in the fourth corridor was adjusted to S * O,4 (t), the dissolved oxygen concentration in the fifth corridor was adjusted to S * O,5 (t).

Claims

1. A knowledge-guided collaborative optimization control method for aeration process in sewage treatment, characterized by: Establish a collaborative optimization control model for the sewage treatment aeration process based on mechanism knowledge, design a collaborative optimization method based on evolutionary knowledge, design a multivariable proportional-integral-differential controller, and realize the optimization set value tracking control, which specifically includes the following steps: (1) Establish a collaborative optimization control model for the aeration process of sewage treatment based on mechanism knowledge Considering the dissolved oxygen concentrations in multiple corridors as decision variables, the aeration energy consumption and effluent quality of the sewage treatment aeration process are taken as optimization control targets: Among them, J EQ (t) is the effluent quality model of the sewage treatment aeration process at time t, J AE (t) is the aeration energy consumption model of the sewage treatment aeration process at time t, z EQ (t) = [S O,3 (t),S O,4 (t),S O,5 (t),S NO (t),S NH (t),SS(t)],z AE (t) = [S O,3 (t),S O,4 (t),S O,5 (t),S NO (t),MLSS(t)],S O,3 (t) is the dissolved oxygen concentration in the third corridor at time t, S O,4 (t) is the dissolved oxygen concentration in the fourth corridor at time t, S O,5 (t) is the dissolved oxygen concentration in the fifth corridor at time t, S O,3 (t), S O,4 (t) and S O,5 (t) is the decision variable, S NO (t) is the nitrate nitrogen concentration at time t, S NH (t) is the ammonia nitrogen concentration at time t, SS(t) is the suspended solids concentration at time t, MLSS(t) is the mixed suspended solids concentration at time t, W EQ,h (t) and W AE,h (t) is the connection weight of the hth kernel function of effluent quality and aeration energy consumption at time t, and is the central value of the hth kernel function of effluent quality and aeration energy consumption at time t, b EQ,h (t) and b AE,h (t) is the width value of the hth kernel function of effluent water quality and aeration energy consumption at time t; Mechanistic knowledge of the wastewater treatment aeration process and feasible ranges of variables as optimization control constraints: Where x(t) = [S O,3 (t),S O,4 (t),S O,5 (t)] is the decision variable vector at time t, S O,4 (t-1) is the dissolved oxygen concentration in the fourth corridor at time t-1, S O,5 (t-1) is the dissolved oxygen concentration in the fifth corridor at time t-1, a4, b4, c4 are related to S O,4 (t) related least squares regression coefficients, a5, b5, c5 are related to S O,5 (t), g1(x(t)) and g2(x(t)) are the constraints at time t established based on the mechanism knowledge, and the material balance equation of dissolved oxygen in multiple corridors is used as the mechanism knowledge: Among them, S * O is the dissolved oxygen saturation concentration, V k is the volume of the kth aerobic corridor, r k is the reaction rate of the kth aerobic corridor, Q k is the flow rate of the kth aerobic corridor, (K L a) k is the oxygen transfer coefficient of the kth aerobic corridor, k = 4,5, S O,k-1 and S O,k The nonlinear relationship is expressed as: S O,k (t)=a k S O,k-1 (t)+b k S O,k (t-1)+c k (5) Among them, a k ,b k ,c k is the least squares regression coefficient associated with the dissolved oxygen concentration in the kth corridor: Where, τ=1,2,…,R, R is the total number of sample data; (2) Designing collaborative optimization methods based on evolutionary knowledge Set the total number of iterations to solve the optimization setpoint to κ max =500, the particle swarm size is Λ=50, x t,n (κ)∈X Γ is the position vector of the nth particle evolving to the κth generation at time t, X Γ is a Γ-dimensional search space; Convert an equality constraint to an inequality constraint: Among them, h1(x(t)) is the inequality constraint after g1(x(t)) is transformed, h2(x(t)) is the inequality constraint after g2(x(t)) is transformed, ε1 and ε2 are the fault tolerance parameters of g1(x(t)) and g2(x(t)): ε1=(1-p1(κ))×max(g1(x(t))) (8) ε2=(1-p2(κ))×max(g2(x(t))) (9) Among them, p1(κ) is the ratio of the number of feasible solutions satisfying g1(x(t)) in the κth generation to the population size in the κth generation, p2(κ) is the ratio of the number of feasible solutions satisfying g2(x(t)) in the κth generation to the population size in the κth generation, and the constraint violation degree is |h1(x t,n (κ))|)+max(0,|h2(x t,n (κ))|), the feasible domain is if Update the particle's velocity: v t,n (k+1)=0.7v t,n (k)+0.5μ1(pBest t,n (k)-x t,n (k))+0.5m2(gBest t (k)-x t,n (k)) (10) Among them, v t,n (κ+1) is the speed vector of the nth particle evolving to the κ+1th generation at time t, μ1 is the individual evolution random number and its value range is [0,1], μ2 is the population evolution random number and its value range is [0,1], pBest t,n (κ) is the best position of the nth particle evolving to the κth generation at time t, gBest t (κ) is the global best position of the κth generation at time t. The non-dominated solution of the κth generation at time t is stored in the external archive, and gBest is selected from the external archive. t (κ); if Change the fitness value of the particle to: in, is J(x t,n (κ)) The fitness value after the change, J(x t,n (κ))=[J EQ (x t,n (κ)),J AE (x t,n (κ))], I = [1,1] T , update the particle speed as: Among them, μ3 is a knowledge evolution random number with a value range of [0,1], p(κ) is the ratio of the number of feasible solutions in the κth generation to the size of the population in the κth generation, K t,n (κ) is the knowledge acquired by evolving to the κth generation at time t: Among them, r is the number of generations of historical evolution recorded, 0 <r<5, and x t,n (κ-r) satisfies: Among them, d n,i (κ-r) is x t,n (κ-r) and x t,n′ Euclidean distance of (κ-r); The particle's position is updated as: x t,n (k+1)=x t,n (k)+v t,n (k+1) (15) Among them, x t,n (κ+1) is the position of particle n when it evolves to the κ+1th generation at time t, x t,i (κ+1) is the position of particle i at time t when it evolves to the κ+1th generation; check whether the evolution process reaches the stopping condition: if the evolutionary generation κ<κ max , the evolutionary number κ increases by 1 and the particle's speed and position are updated again; if the evolutionary number κ = κ max , terminate the evolution process, from the κth max A solution is randomly selected from the solutions of the previous generation as the optimal setting value y of the process variable * (t) = [S * O,3 (t),S * O,4 (t),S * O,5 (t)],S * O,3 (t) is the optimal set value of dissolved oxygen in the third corridor at time t, S * O,4 (t) is the optimal set value of dissolved oxygen in the fourth corridor at time t, S * O,5 (t) is the optimal set value of dissolved oxygen in the fifth corridor at time t; (3) Design of multivariable proportional-integral-derivative controller Using a multivariable proportional-integral-derivative controller to optimize the setpoint y * (t) Conduct tracking control: Where, Δu(t)=[ΔK L a3(t),ΔK L a4(t),ΔK L a5(t)] T is the operating variable matrix at time t, ΔK L a3(t) is the change in the dissolved oxygen transfer coefficient of the third corridor at time t, ΔK L a4(t) is the change in the dissolved oxygen transfer coefficient of the fourth corridor at time t, ΔK L a5(t) is the change in the dissolved oxygen transfer coefficient of the fifth corridor at time t, C = [200, 200, 200] is the proportional coefficient, L = [15, 15, 15] is the integral time constant, F = [2, 2, 2] is the differential time constant, e(t) = y(t)-y * (t) is the control error vector at time t, y(t) = [S O,3 (t),S O,4 (t),S O,5 (t)] is the actual output at time t; Use Δu(t) to adjust the dissolved oxygen transfer coefficient and internal recirculation rate: Among them, K L a3(t+1) is the dissolved oxygen transfer coefficient of the third corridor at time t+1, K L a3(t) is the dissolved oxygen transfer coefficient of the third corridor at time t, K L a4(t+1) is the dissolved oxygen transfer coefficient of the fourth corridor at time t+1, K L a4(t) is the dissolved oxygen transfer coefficient of the fourth corridor at time t, K L a5(t+1) is the dissolved oxygen transfer coefficient of the fifth corridor at time t+1, K L a5(t) is the dissolved oxygen transfer coefficient of the fifth corridor at time t; The input of the sewage treatment aeration process optimization control system at time t is Δu(t), and by operating ΔK L a3(t) realizes S O,3 (t) tracking control, by operating ΔK L a4(t) realizes S O,4 (t) tracking control, by operating ΔK L a5(t) realizes S O,5 (t) tracking control, the dissolved oxygen concentration in the third corridor is adjusted to S * O,3 (t), the dissolved oxygen concentration in the fourth corridor was adjusted to S * O,4 (t), the dissolved oxygen concentration in the fifth corridor was adjusted to S * O,5 (t).

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