A knowledge-guided collaborative optimization control method for wastewater treatment aeration process

Abstract

The application provides a sewage treatment aeration process collaborative optimization control method based on knowledge guidance, realizes optimization control on dissolved oxygen in different corridors, and achieves the purposes of improving effluent water quality and reducing aeration energy consumption. The method establishes a sewage treatment aeration process collaborative optimization control model based on mechanism knowledge, obtains the relationship between dissolved oxygen concentrations in different corridors and effluent water quality and aeration energy consumption and the relationship between dissolved oxygen concentrations in different corridors, designs a collaborative optimization method based on evolution knowledge, obtains dissolved oxygen concentration set values which can effectively reduce aeration energy consumption and improve effluent water quality, and designs a multivariable proportional-integral-derivative controller to realize tracking control of the optimized set values.

Description

Technical Field

[0001] This invention addresses the optimization of aeration processes in wastewater treatment by designing a knowledge-guided collaborative optimization control method. It establishes a mechanistic knowledge-based collaborative optimization control model for the aeration process, designs an evolutionary knowledge-based collaborative optimization method to obtain optimal setpoints, and designs a multivariable proportional-integral-derivative controller to track and control these setpoints. This method reduces aeration energy consumption while ensuring effluent quality, which is significant for the efficient and stable operation of wastewater treatment processes. This invention belongs to both the field of water research and the field of intelligent optimization control. Background Technology

[0002] The rapid pace of industrialization and urbanization in my country has led to a year-on-year increase in domestic sewage discharge, placing new demands on sewage treatment capacity and presenting it with the dual challenges of achieving discharge standards and conserving energy. Sewage treatment aeration processes continuously oxidize ammonia nitrogen to nitrate nitrogen, thereby reducing the concentration of ammonia nitrogen in the effluent. However, the energy consumption of aeration accounts for more than half of the operating energy consumption of sewage treatment plants, making it a crucial part of the sewage treatment process. To improve operational effectiveness and efficiency, optimized control strategies have been widely applied to sewage treatment aeration processes.

[0003] The goal of optimizing the aeration process in wastewater treatment is to ensure effluent quality meets standards and reduce aeration energy consumption. However, the operational mechanism of wastewater treatment aeration is complex, with strong conflicts between effluent quality and aeration energy consumption, and multiple channels of dissolved oxygen interacting, making it difficult to solve. Therefore, how to solve for the optimal setpoint of the wastewater treatment aeration process to achieve effluent quality compliance while reducing aeration energy consumption is an important research topic. In establishing models for effluent quality and aeration energy consumption, the complex nonlinear dynamics of the wastewater treatment process and the mutual influence of dissolved oxygen in multiple channels make it difficult to accurately express the optimization model using a mechanistic model. Therefore, a data- and knowledge-based modeling method is adopted to accurately describe the optimization objectives and constraints of the wastewater treatment process. Furthermore, the conflicting objectives and complex constraints in the wastewater treatment process affect the quality of the optimal setpoint solution, easily leading to poor optimization control performance. Therefore, designing a knowledge- and data-based optimization control method can not only improve the quality of the optimal setpoint solution, thus ensuring effluent quality while reducing energy consumption, but also guarantee the stable and efficient operation of the wastewater treatment aeration process.

[0004] This invention analyzes the characteristics of the wastewater treatment aeration process, establishes operational performance indicators and constraints including effluent quality and aeration energy consumption, designs a collaborative optimization method based on evolutionary knowledge, obtains effective values ​​for reducing aeration energy consumption and increasing dissolved oxygen concentration in effluent, and achieves efficient and stable operation of the wastewater treatment aeration process. Summary of the Invention

[0005] This invention proposes a knowledge-guided collaborative optimization control method for wastewater treatment aeration processes, which reduces aeration energy consumption while ensuring effluent quality. The method establishes a collaborative optimization control model for wastewater treatment aeration processes based on mechanistic knowledge, designs a collaborative optimization method based on evolutionary knowledge, and uses a multivariate proportional-integral-derivative controller to control process variables to track and optimize setpoints in real time.

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

[0007] 1. A knowledge-guided collaborative optimization control method for wastewater treatment aeration processes, specifically including the following steps:

[0008] (1) Establish a collaborative optimization control model for the aeration process in wastewater treatment based on mechanistic knowledge.

[0009] Considering the dissolved oxygen concentration in multiple corridors as decision variables, and the aeration energy consumption and effluent quality in the wastewater treatment aeration process as optimization control objectives:

[0010]

[0011] Among them, J EQ (t) represents the effluent water quality model of the wastewater treatment aeration process at time t, J AE (t) represents the aeration energy consumption model for the wastewater 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) represents the dissolved oxygen concentration in the third corridor at time t, S O,4 (t) represents the dissolved oxygen concentration in the fourth corridor at time t, S O,5 (t) represents 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) represents the concentration of nitrate nitrogen at time t, S NH (t) represents the ammonia nitrogen concentration at time t, SS(t) represents the suspended solids concentration at time t, MLSS(t) represents the mixed suspended solids concentration at time t, and W EQ,h (t) and W AE,h(t) represents the connection weights of the h-th kernel function for effluent water quality and aeration energy consumption at time t. and Let b be the center value of the h-th kernel function of effluent water quality and aeration energy consumption at time t. EQ,h (t) and b AE,h (t) represents the width of the h-th kernel function for effluent water quality and aeration energy consumption at time t;

[0012] The mechanistic knowledge of the wastewater treatment aeration process and the feasible range of variables serve 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) represents the dissolved oxygen concentration in the fourth corridor at time t-1, S O,5 (t-1) represents the dissolved oxygen concentration in the fifth corridor at time t-1, and a4, b4, c4 are the values ​​related to S. O,4 (t) are the least squares regression coefficients related to S, where a5, b5, c5 are the coefficients related to S. O,5 The least squares regression coefficients related to (t), g1(x(t)) and g2(x(t)) are the constraints at time t established based on mechanistic knowledge, and the material balance equations of dissolved oxygen in multiple corridors are used as mechanistic knowledge:

[0015]

[0016] Among them, S * O V represents the dissolved oxygen saturation concentration. k Let r be the volume of the k-th aerobic corridor. k Let Q be the reaction rate of the k-th aerobic corridor. k Let K be the flow velocity of the k-th aerobic corridor. L a) k Let S be the oxygen transfer coefficient of the k-th aerobic corridor, where k = 4, 5. 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 The least squares regression coefficients related to the dissolved oxygen concentration in the k-th corridor are:

[0019]

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

[0021] (2) Design a collaborative optimization method based on evolutionary knowledge

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

[0023] Convert equality constraints to inequality constraints:

[0024]

[0025] Where h1(x(t)) is the inequality constraint after transforming g1(x(t)), h2(x(t)) is the inequality constraint after transforming g2(x(t)), and 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] Where p1(κ) is the ratio of the number of feasible solutions satisfying g1(x(t)) in generation κ to the population size in generation κ, p2(κ) is the ratio of the number of feasible solutions satisfying g2(x(t)) in generation κ to the population size in generation κ, and the constraint violation degree is... feasible region is

[0029] if The velocity of the updated particles is:

[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 velocity vector of the nth particle evolving to the (κ+1)th generation at time t, μ1 is a random number for individual evolution with a value range of [0,1], μ2 is a random number for population evolution with a value range of [0,1], and pBest t,n (κ) represents the optimal position of the nth particle at time t in the κth generation, and gBest t (κ) represents the global best position at time t for generation κ. The non-dominated solution at time t for generation κ is stored in an external file, and gBest is selected in the external file. t (κ);

[0032] if Change the particle's fitness value:

[0033]

[0034] in, For J(x) t,n The fitness value after the change of (κ)), J(x) t,n (κ))=[J EQ (x t,n (κ)),J AE (x t,n (κ))],I=[1,1] T The velocity of the updated particles is:

[0035]

[0036] Where μ3 is a random number for knowledge evolution and its value ranges from [0,1], p(κ) is the ratio of the number of feasible solutions in generation κ to the population size in generation κ, and K tn (κ) represents the knowledge acquired at time t by the κth generation:

[0037]

[0038] Where r is the number of generations of the recorded evolutionary history, 0 <r<5, and x t,n (κ-r) satisfies:

[0039]

[0040] Where, 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 follows:

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

[0043] Where, x t,n (κ+1) represents the position of particle n at time t in the (κ+1)th generation, x t,i (κ+1) represents the position of particle i at time t in the (κ+1)th generation; the evolutionary process is checked to see if the stopping condition is met: if the number of generations κ < κ max The evolutionary generation κ increases by 1 and the particle's velocity and position are updated again; if the evolutionary generation κ = κ... max Terminate the evolutionary process, starting from the κth max One solution is randomly selected from the solutions to be used as the optimal setpoint y for the process variable. * (t)=[S * O,3 (t),S * O,4 (t),S * O,5 [(t)],S * O,3 (t) represents the optimal dissolved oxygen setpoint for the third corridor at time t, S * O,4 (t) represents the optimal dissolved oxygen setting for the fourth corridor at time t, S * O,5 (t) represents the optimal dissolved oxygen setting for the fifth corridor at time t;

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

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

[0046]

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

[0048] Adjust the dissolved oxygen transfer coefficient and internal recirculation flow rate using Δu(t):

[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 to the wastewater treatment aeration process optimization control system at time t is Δu(t), and the input is determined by operating ΔK. L a3(t) realizes the S O,3 Tracking control of (t) is achieved by operating ΔK. L a4(t) realizes the S O,4 Tracking control of (t) is achieved by operating ΔK. L a5(t) realizes the S O,5 If the tracking control of (t) is applied, the dissolved oxygen concentration in the third corridor will be 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). Attached Figure Description

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

[0053] Figure 2 The fourth corridor dissolved oxygen concentration S of this invention O,4 Optimize the control effect diagram and error diagram.

[0054] Figure 3 The fifth corridor dissolved oxygen concentration S of this invention O,5 Optimize the control effect diagram and error diagram. Detailed Implementation

[0055] 1. A knowledge-guided collaborative optimization control method for wastewater treatment aeration processes, specifically including the following steps:

[0056] (1) Establish a collaborative optimization control model for the aeration process in wastewater treatment based on mechanistic knowledge.

[0057] Considering the dissolved oxygen concentration in multiple corridors as decision variables, and the aeration energy consumption and effluent quality in the wastewater treatment aeration process as optimization control objectives:

[0058]

[0059] Among them, J EQ (t) represents the effluent water quality model of the wastewater treatment aeration process at time t, J AE (t) represents the aeration energy consumption model for the wastewater 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) represents the dissolved oxygen concentration in the third corridor at time t, S O,4 (t) represents the dissolved oxygen concentration in the fourth corridor at time t, S O,5 (t) represents 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) represents the concentration of nitrate nitrogen at time t, S NH (t) represents the ammonia nitrogen concentration at time t, SS(t) represents the suspended solids concentration at time t, MLSS(t) represents the mixed suspended solids concentration at time t, and W EQ,h (t) and W AE,h (t) represents the connection weights of the h-th kernel function for effluent water quality and aeration energy consumption at time t. and Let b be the center value of the h-th kernel function of effluent water quality and aeration energy consumption at time t. EQ,h (t) and b AE,h (t) represents the width of the h-th kernel function for effluent water quality and aeration energy consumption at time t;

[0060] The mechanistic knowledge of the wastewater treatment aeration process and the feasible range of variables serve 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) represents the dissolved oxygen concentration in the fourth corridor at time t-1, S O,5 (t-1) represents the dissolved oxygen concentration in the fifth corridor at time t-1, and a4, b4, c4 are the values ​​related to S. O,4 (t) are the least squares regression coefficients related to S, where a5, b5, c5 are the coefficients related to S. O,5 The least squares regression coefficients related to (t), g1(x(t)) and g2(x(t)) are the constraints at time t established based on mechanistic knowledge, and the material balance equations for dissolved oxygen in multiple corridors are used as mechanistic knowledge:

[0063]

[0064] Among them, S * O V represents the dissolved oxygen saturation concentration. k Let r be the volume of the k-th aerobic corridor. k Let Q be the reaction rate of the k-th aerobic corridor. k Let K be the flow velocity of the k-th aerobic corridor. L a) k Let S be the oxygen transfer coefficient of the k-th aerobic corridor, where k = 4, 5. 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 kThe least squares regression coefficients related to the dissolved oxygen concentration in the k-th corridor are:

[0067]

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

[0069] (2) Design a collaborative optimization method based on evolutionary knowledge

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

[0071] Convert equality constraints to inequality constraints:

[0072]

[0073] Where h1(x(t)) is the inequality constraint after transforming g1(x(t)), h2(x(t)) is the inequality constraint after transforming g2(x(t)), and ε1 and ε2 are the fault tolerance parameters of g1(x(t)) and g2(x(t)).

[0074]

[0075] Where 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 ratio of 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 population size of generation κ is the constraint violation degree. feasible region is

[0077] if The velocity of the updated particles is:

[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 velocity vector of the nth particle evolving to the (κ+1)th generation at time t, μ1 is a random number for individual evolution with a value range of [0,1], μ2 is a random number for population evolution with a value range of [0,1], and pBest t,n (κ) represents the optimal position of the nth particle at time t in the κth generation, and gBest t (κ) represents the global best position at time t for generation κ. The non-dominated solution at time t for generation κ is stored in an external file, and gBest is selected in the external file. t (κ);

[0080] if Change the particle's fitness value:

[0081]

[0082] in, For J(x) t,n The fitness value after the change of (κ)), J(x) t,n (κ))=[J EQ (x t,n (κ)),J AE (x t,n (κ))],I=[1,1] T The velocity of the updated particles is:

[0083]

[0084] Where μ3 is a random number for knowledge evolution and its value ranges from [0,1], p(κ) is the ratio of the number of feasible solutions in generation κ to the population size in generation κ, and K t,n (κ) represents the knowledge acquired at time t by the κth generation:

[0085]

[0086] Where r is the number of generations of the recorded evolutionary history, 0 <r<5, and x t,n (κ-r) satisfies:

[0087]

[0088] Where, 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 follows:

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

[0091] Where, x t,n (κ+1) represents the position of particle n at time t in the (κ+1)th generation, x t,i (κ+1) represents the position of particle i at time t in the (κ+1)th generation; the evolutionary process is checked to see if the stopping condition is met: if the number of generations κ < κ max The evolutionary generation κ increases by 1 and the particle's velocity and position are updated again; if the evolutionary generation κ = κ... max Terminate the evolutionary process, starting from the κth max One solution is randomly selected from the solutions to be used as the optimal setpoint y for the process variable. * (t)=[S * O,3 (t),S * O,4 (t),S * O,5 [(t)],S * O,3 (t) represents the optimal dissolved oxygen setpoint for the third corridor at time t, S * O,4 (t) represents the optimal dissolved oxygen setting for the fourth corridor at time t, S * O,5 (t) represents the optimal dissolved oxygen setting for the fifth corridor at time t;

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

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

[0094]

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

[0096] Adjust the dissolved oxygen transfer coefficient and internal recirculation flow rate using Δu(t):

[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 to the wastewater treatment aeration process optimization control system at time t is Δu(t), and the input is determined by operating ΔK. L a3(t) realizes the S O,3 Tracking control of (t) is achieved by operating ΔK. L a4(t) realizes the S O,4 Tracking control of (t) is achieved by operating ΔK. L a5(t) realizes the S O,5 If the tracking control of (t) is applied, the dissolved oxygen concentration in the third corridor will be 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 wastewater treatment aeration processes, characterized in that: A collaborative optimization control model for the aeration process in wastewater treatment based on mechanistic knowledge is established. A collaborative optimization method based on evolutionary knowledge is designed, and a multivariable proportional-integral-derivative controller is designed to achieve optimal setpoint tracking control. The specific steps include: (1) Establish a collaborative optimization control model for the aeration process in wastewater treatment based on mechanistic knowledge. Considering the dissolved oxygen concentration in multiple corridors as decision variables, and the aeration energy consumption and effluent quality in the wastewater treatment aeration process as optimization control objectives: Among them, J EQ (t) represents the effluent water quality model of the wastewater treatment aeration process at time t, J AE (t) represents the aeration energy consumption model for the wastewater 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) represents the dissolved oxygen concentration in the third corridor at time t, S O,4 (t) represents the dissolved oxygen concentration in the fourth corridor at time t, S O,5 (t) represents 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) represents the concentration of nitrate nitrogen at time t, S NH (t) represents the ammonia nitrogen concentration at time t, SS(t) represents the suspended solids concentration at time t, MLSS(t) represents the mixed suspended solids concentration at time t, and W EQ,h (t) and W AE,h (t) represents the connection weights of the h-th kernel function for effluent water quality and aeration energy consumption at time t. and Let b be the center value of the h-th kernel function of effluent water quality and aeration energy consumption at time t. EQ,h (t) and b AE,h (t) represents the width of the h-th kernel function for effluent water quality and aeration energy consumption at time t; The mechanistic knowledge of the wastewater treatment aeration process and the feasible range of variables serve 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) represents the dissolved oxygen concentration in the fourth corridor at time t-1, S O,5 (t-1) represents the dissolved oxygen concentration in the fifth corridor at time t-1, and a4, b4, c4 are the values ​​related to S. O,4 (t) are the least squares regression coefficients related to S, where a5, b5, c5 are the coefficients related to S. O,5 The least squares regression coefficients related to (t), g1(x(t)) and g2(x(t)) are the constraints at time t established based on mechanistic knowledge, and the material balance equations of dissolved oxygen in multiple corridors are used as mechanistic knowledge: Among them, S * O V represents the dissolved oxygen saturation concentration. k Let r be the volume of the k-th aerobic corridor. k Let Q be the reaction rate of the k-th aerobic corridor. k Let K be the flow velocity of the k-th aerobic corridor. L a) k Let S be the oxygen transfer coefficient of the k-th aerobic corridor, where k = 4, 5. 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 The least squares regression coefficients related to the dissolved oxygen concentration in the k-th corridor are: Where τ = 1, 2, ..., R, and R is the total number of sample data; (2) Design a collaborative optimization method based on evolutionary knowledge Set the total number of iterations for solving the optimization setpoint to κ. max =500, the particle swarm size is Λ=50, x t,n (κ)∈X Γ Let X be the position vector of the nth particle evolving to the κth generation at time t. Γ For a Γ-dimensional search space; Convert equality constraints to inequality constraints: Where h1(x(t)) is the inequality constraint after transforming g1(x(t)), h2(x(t)) is the inequality constraint after transforming g2(x(t)), and 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) Where p1(κ) is the ratio of the number of feasible solutions satisfying g1(x(t)) in generation κ to the population size in generation κ, p2(κ) is the ratio of the number of feasible solutions satisfying g2(x(t)) in generation κ to the population size in generation κ, and the constraint violation degree is... |h1(x t,n (κ))|)+max(0,|h2(x t,n (κ))|), the feasible region is if The velocity of the updated particles is: 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 velocity vector of the nth particle evolving to the (κ+1)th generation at time t, μ1 is a random number for individual evolution with a value range of [0,1], μ2 is a random number for population evolution with a value range of [0,1], and pBest t,n (κ) represents the optimal position of the nth particle at time t in the κth generation, and gBest t (κ) represents the global best position at time t for generation κ. The non-dominated solution at time t for generation κ is stored in an external file, and gBest is selected in the external file. t (κ); if Change the particle's fitness value: in, For J(x) t,n The fitness value after the change of (κ)), J(x) t,n (κ))=[J EQ (x t,n (κ)),J AE (x t,n (κ))],I=[1,1] T The velocity of the updated particles is: Where μ3 is a random number for knowledge evolution and its value ranges from [0,1], p(κ) is the ratio of the number of feasible solutions in generation κ to the population size in generation κ, and K t,n (κ) represents the knowledge acquired at time t to the κth generation: Where r is the number of generations of the recorded evolutionary history, 0 <r<5, and x t,n (κ-r) satisfies: Where, 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 follows: x t,n (k+1)=x t,n (k)+v t,n (k+1) (15) Where, x t,n (κ+1) represents the position of particle n at time t in the (κ+1)th generation, x t,i (κ+1) represents the position of particle i at time t in the (κ+1)th generation; the evolutionary process is checked to see if the stopping condition is met: if the number of generations κ < κ max The evolutionary generation κ increases by 1 and the particle's velocity and position are updated again; if the evolutionary generation κ = κ... max Terminate the evolutionary process, starting from the κth max One solution is randomly selected from the solutions to be used as the optimal setpoint y for the process variable. * (t)=[S * O,3 (t),S * O,4 (t),S * O,5 [(t)],S * O,3 (t) represents the optimal dissolved oxygen setpoint for the third corridor at time t, S * O,4 (t) represents the optimal dissolved oxygen setting for the fourth corridor at time t, S * O,5 (t) represents the optimal dissolved oxygen setting for the fifth corridor at time t; (3) Design a multivariable proportional-integral-derivative controller Using a multivariable proportional-integral-derivative controller to optimize the setpoint y * (t) Perform tracking control: Where, Δu(t)=[ΔK L a3(t),ΔK L a4(t),ΔK L a5(t)] T Let ΔK be the matrix of operations at time t. L a3(t) represents the change in the dissolved oxygen transfer coefficient of the third corridor at time t, ΔK L a4(t) represents the change in the dissolved oxygen transfer coefficient of the fourth corridor at time t, ΔK L a5(t) represents the change in dissolved oxygen transfer coefficient in the fifth corridor at time t, C = [200, 200, 200] is the proportionality coefficient, L = [15, 15, 15] is the integral time constant, F = [2, 2, 2] is the differential time constant, and e(t) = y(t) - y * y(t) is the control error vector at time t, and y(t) = [S O,3 (t),S O,4 (t),S O,5 [(t)] represents the actual output at time t; Adjust the dissolved oxygen transfer coefficient and internal recirculation flow rate using Δu(t): 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 to the wastewater treatment aeration process optimization control system at time t is Δu(t), and the input is determined by operating ΔK. L a3(t) realizes the S O,3 Tracking control of (t) is achieved by operating ΔK. L a4(t) realizes the S O,4 Tracking control of (t) is achieved by operating ΔK. L a5(t) realizes the S O,5 If the tracking control of (t) is applied, the dissolved oxygen concentration in the third corridor will be 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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