A method for optimizing control of sewage treatment process based on multi-task constraints

Abstract

A sewage treatment process optimization control method based on multi-task constraints belongs to the field of sewage treatment. In order to realize multi-task constraint parallel optimization control in the sewage treatment process, the present invention constructs a multi-task constraint optimization model for the sewage treatment process, describes the optimization objectives of the denitrification task and the phosphorus removal task with effluent water quality constraints, designs a sewage treatment process multi-task particle swarm optimization setting method based on an adaptive penalty function, solves the optimized setting values ​​of dissolved oxygen, nitrate nitrogen, methanol flow and polyaluminium chloride flow in the sewage treatment process, and designs a multi-task controller to complete the tracking control of the optimized setting values, thereby promoting the parallel constraint optimization of the biological denitrification task and the biological phosphorus removal task in the sewage treatment process.

Description

Technical Field

[0001] The present invention designs a sewage treatment process optimization control method based on multi-task constraints based on the analysis of the operation characteristics of the sewage treatment process, wherein a sewage treatment process optimization target model with multi-task constraints is established, a sewage treatment process multi-task constraint optimization setting method based on an adaptive penalty function is studied, and a controller is designed to implement tracking control of multi-task optimization setting values, thereby promoting the parallel optimization of biological denitrification tasks and biological phosphorus removal tasks in the sewage treatment process. This sewage treatment process multi-task optimization control method based on multi-task constraints belongs to the field of water treatment. Background Art

[0002] In the process of urban sewage treatment, denitrification and dephosphorization are two tasks that influence and restrict each other. These two tasks have different optimization operation requirements, different operation constraints and multiple conflicting optimization objectives. Therefore, how to design a multi-task constrained optimization control strategy to achieve the parallel optimization of denitrification and dephosphorization tasks is the key to achieving the optimal operation of the sewage treatment process. Establishing a multi-task constrained optimization target model for the sewage treatment process is of great significance for accurately describing the optimization objectives and constraints of different tasks in the sewage treatment process; at the same time, how to transfer knowledge between denitrification and dephosphorization tasks with effluent water quality constraints, promote the parallel optimization of multiple tasks, and obtain the optimized set values ​​is the key issue to achieve the constrained multi-task optimization operation of the sewage treatment process; therefore, designing a reasonable constrained multi-task optimization control method, achieving the parallel optimization of denitrification and dephosphorization tasks, obtaining the optimized set values ​​of dissolved oxygen, nitrate nitrogen, methanol flow and polyaluminium chloride flow, and tracking control is the key to the stable and efficient operation of the sewage treatment process.

[0003] The present invention designs a sewage treatment process optimization control method based on multi-task constraints, mainly establishes a sewage treatment process optimization target model with multi-task constraints, designs a sewage treatment process multi-task constraint optimization setting method based on adaptive penalty function, and designs a controller to realize tracking control of multi-task optimization setting values, so as to promote the parallel optimization of biological denitrification tasks and biological phosphorus removal tasks in the sewage treatment process. Summary of the invention

[0004] The present invention obtains a sewage treatment process optimization control method based on multi-task constraints. The method establishes a multi-task constraint optimization model for the sewage treatment process, obtains the optimization objective functions of the denitrification task and the phosphorus removal task with effluent water quality constraints, designs a sewage treatment process multi-task constraint optimization setting method based on an adaptive penalty function to solve the optimized setting value of the sewage treatment process, and designs a controller to track and control the optimized setting value, thereby realizing multi-task constraint optimization control of the sewage treatment process.

[0005] The present invention adopts the following technical solutions and implementation steps:

[0006] 1. A sewage treatment process optimization control method based on multi-task constraints, characterized in that a sewage treatment process optimization target model with multi-task constraints is established, a sewage treatment process multi-task constraint optimization setting method based on an adaptive penalty function is studied, and a controller is designed to implement tracking control of multi-task optimization setting values, thereby promoting the parallel optimization of biological denitrification tasks and biological phosphorus removal tasks in the sewage treatment process, comprising the following steps:

[0007] (1) Establishing a multi-task constrained sewage treatment process optimization target model

[0008] The multi-task constrained optimization target model of the sewage treatment process describes the optimization targets of the biological denitrification task and the biological phosphorus removal task through an adaptive kernel function; the constrained optimization target model of the biological denitrification task is

[0009]

[0010] Among them, f1(x N (t)) is the constrained optimization target energy consumption model of the biological denitrification task in the sewage treatment process at time t, x N (t) = [S O (t), S NO (t), M F (t), S NH (t)] is the input variable of the denitrification task optimization model at time t, Ω N is the decision space of the optimization target model of denitrification task, S O (t) is the aerobic terminal dissolved oxygen concentration at time t and its value range is [0.4, 3], in mg / L, S NO (t) is the anaerobic terminal nitrate nitrogen concentration at time t and its value range is [0.3, 2], unit is mg / L, M F (t) is the methanol flow rate at time t and its value range is [60, 400], unit is liter / hour, S NH (t) is the effluent ammonia nitrogen concentration at time t and its value range is [0, 2.5], unit is mg / L, a1(t) is the output offset of the denitrification task energy consumption model of the sewage treatment process at time t and its value range is [-2, 2], W 1,a (t) is the weight of the ath adaptive kernel function of the energy consumption model of the denitrification task in the sewage treatment process at time t and its value range is [-3, 3], c 1,a (t) = [c 1,a,1 (t), c 1,a,2 (t), c 1,a,3 (t), c 1,a,4 (t)] Tis the center vector of the ath adaptive kernel function of the denitrification task energy consumption model of the sewage treatment process at time t, c 1,a,1 (t) is the first dimension center of the ath adaptive kernel function of the denitrification task energy consumption model of the sewage treatment process at time t and its value range is [-1, 1], c 1,a,2 (t) is the second dimensional center of the ath adaptive kernel function of the denitrification task energy consumption model of the sewage treatment process at time t and its value range is [-1, 1], c 1,a,3 (t) is the third-dimensional center of the ath adaptive kernel function of the denitrification task energy consumption model of the sewage treatment process at time t and its value range is [-1, 1], c 1,a,4 (t) is the fourth-dimensional center of the ath adaptive kernel function of the energy consumption model of the denitrification task in the sewage treatment process at time t and its value range is [-1, 1], T is the transpose of the vector or matrix, σ 1,a (t) is the width of the ath adaptive kernel function of the denitrification task energy consumption model at time t and its value range is [0, 2]; f N (x N (t)) is the constraint condition of the denitrification task of the sewage treatment process at time t, and the total nitrogen in the effluent

[0011]

[0012] Among them, a2(t) is the output offset of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t and its value range is [-2, 2], W 2,a (t) is the weight of the ath adaptive kernel function of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t and its value range is [-3, 3], c 2,a (t) = [c 2,a,1 (t), c 2,a,2 (t), c 2,a,3 (t), c 2,a,4 (t)] T is the center vector of the ath adaptive kernel function of the total nitrogen model of the effluent from the wastewater treatment process at time t, c 2,a,1 (t) is the first dimension center of the ath adaptive kernel function of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t, and its value range is [-1, 1], c 2,a,2 (t) is the second dimension center of the ath adaptive kernel function of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t, and its value range is [-1, 1], c 2,a,3 (t) is the third-dimensional center of the ath adaptive kernel function of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t, and its value range is [-1, 1], c 2,a,4 (t) is the fourth dimension center of the ath adaptive kernel function of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t, and its value range is [-1, 1], σ 2,a(t) is the width of the ath adaptive kernel function of the total nitrogen model of the denitrification task effluent at time t and its value range is [0, 2];

[0013] Parameter update of denitrification task constraint optimization model in sewage treatment process:

[0014]

[0015] Where A(t) = [a1(t), a2(t), W 1,a (t), W 2,a (t), c 1,a,1 (t), c 1,a,2 (t), c 1,a,3 (t), c 1,a,4 (t), c 2,a,1 (t), c 2,a,2 (t), c 2,a,3 (t), c 2,a,4 (t),σ 1,a (t), σ 2,a (t)] is the parameter of the denitrification task constraint optimization model of the sewage treatment process at time t; α1 is the learning rate of the parameter of the denitrification task constraint optimization model of the sewage treatment process and its value range is [0, 1]; e 1u (t) = y1(t) - y 1u (t) is the prediction error of the denitrification task constraint optimization model of the sewage treatment process at time t, y1(t) is the output of the denitrification task constraint optimization model of the sewage treatment process at time t, y 1u (t) = [EC N (t), TN(t)] is the actual output value of the denitrification task of the sewage treatment process at time t, EC(t) is the actual energy consumption value of the denitrification task of the sewage treatment process at time t, and TN(t) is the actual effluent total nitrogen value of the sewage treatment process at time t;

[0016] The constrained optimization objective model of biological phosphorus removal task is

[0017]

[0018] Among them, f2(x P (t)) is the constrained optimization target energy consumption model of biological phosphorus removal task in sewage treatment process at time t, x P (t) = [S O (t), S NO (t), M P (t), MLSS(t)] is the input variable of the optimization model for phosphorus removal task at time t, Ω P is the decision space of the optimization target model for phosphorus removal task, M P(t) is the polyaluminium chloride flow rate at time t and its value range is [0, 108], unit is liter / hour, MLSS(t) is the effluent mixed suspended solids concentration at time t and its value range is [0, 100], unit is mg / L, b1(t) is the output offset of the energy consumption model of the phosphorus removal task in the sewage treatment process at time t and its value range is [-2, 2], W 1,b (t) is the weight of the bth adaptive kernel function of the energy consumption model of phosphorus removal task in sewage treatment process at time t and its value range is [-3, 3], c 1,b (t) = [c 1,b,1 (t), c 1,b,2 (t), c 1,b,3 (t), c 1,b,4 (t)] T is the center vector of the bth adaptive kernel function of the energy consumption model of phosphorus removal task in sewage treatment process at time t, c 1,b,1 (t) is the first dimension center of the bth adaptive kernel function of the energy consumption model of phosphorus removal task in the sewage treatment process at time t and its value range is [-1, 1], c 1,b,2 (t) is the second dimensional center of the bth adaptive kernel function of the energy consumption model of phosphorus removal task in the sewage treatment process at time t and its value range is [-1, 1], c 1,b,3 (t) is the third-dimensional center of the b-th adaptive kernel function of the energy consumption model of phosphorus removal task in the sewage treatment process at time t and its value range is [-1, 1], c 1,b,4 (t) is the fourth dimension center of the bth adaptive kernel function of the energy consumption model of phosphorus removal task in sewage treatment process at time t and its value range is [-1, 1], σ 1,b (t) is the width of the bth adaptive kernel function of the energy consumption model of the phosphorus removal task at time t and its value range is [0, 2]; f TP (x P (t)) is the constraint condition of phosphorus removal task in sewage treatment process at time t, total phosphorus in effluent

[0019]

[0020] Where b2(t) is the output offset of the total phosphorus model of the effluent of the sewage treatment process at time t and its value range is [-2, 2], W 2,b (t) is the weight of the bth adaptive kernel function of the total phosphorus model of the effluent of the sewage treatment process at time t and its value range is [-3, 3], c 2,b (t) = [c 2,b,1 (t), c 2,b,2 (t), c 2,b,3 (t), c 2,b,4 (t)] T is the center vector of the bth adaptive kernel function of the effluent total phosphorus model of the sewage treatment process for phosphorus removal task at time t, c1,b,1 (t) is the first dimension center of the bth adaptive kernel function of the total phosphorus model of the effluent of the sewage treatment process for phosphorus removal task at time t, and its value range is [-1, 1], c 1,b,2 (t) is the second dimension center of the b-th adaptive kernel function of the total phosphorus model of the effluent of the sewage treatment process at time t, and its value range is [-1, 1], c 1,b,3 (t) is the third-dimensional center of the b-th adaptive kernel function of the total phosphorus model of the effluent of the sewage treatment process for phosphorus removal task at time t, and its value range is [-1, 1], c 1,b,4 (t) is the fourth dimension center of the b-th adaptive kernel function of the total phosphorus model of the effluent of the sewage treatment process at time t, and its value range is [-1, 1], σ 2,b (t) is the width of the bth adaptive kernel function of the total phosphorus model of the phosphorus removal task effluent at time t and its value range is [0, 2];

[0021] Parameter update of phosphorus removal task constraint optimization model in sewage treatment process:

[0022]

[0023] Where, B(t) = [b1(t), b2(t), W 1,b (t), W 2,b (t), c 1,b,1 (t), c 1,b,2 (t), c 1,b,3 (t), c 1,b,4 (t), c 2,b,1 (t), c 2,b,2 (t), c 2,b,3 (t), c 2,b,4 (t),σ 1,b (t),σ 2,b (t)] is the parameter of the optimization model for phosphorus removal task constraints in the sewage treatment process at time t; α2 is the learning rate of the optimization model for phosphorus removal task constraints in the sewage treatment process and its value range is [0, 1]; e 2u (t) = y2(t) - y 2u (t) is the prediction error of the optimization model for phosphorus removal task constraints in the sewage treatment process at time t, y2(t) is the output of the optimization model for phosphorus removal task constraints in the sewage treatment process at time t, y 2u (t) = [EC P (t), TP(t)] is the actual output value of the phosphorus removal task of the sewage treatment process at time t, EC(t) is the actual energy consumption value of the phosphorus removal task of the sewage treatment process at time t, and TP(t) is the actual effluent total phosphorus value of the sewage treatment process at time t;

[0024] (2) Solving the optimal setpoints for the sewage treatment process with multi-task constraints

[0025] Design a constrained multi-task particle swarm algorithm based on adaptive penalty function to solve the optimal set value of the sewage treatment process, taking into account the fitness value and constraint violation degree, and obtain the multi-task optimal set value;

[0026] ① Set the particle swarm size of each task in the multi-task particle swarm optimization process to 100, the number of tasks to 2, the total number of iterations to 1000, and initialize the external archive Θ t (0) is an empty set, and the position, velocity and skill factor of the particle are randomly initialized. The multi-task constrained optimization target model of the sewage treatment process is F(x(t))=[f1(x N (t)), f2(x P (t))] is used as the optimization objective of the multi-task particle swarm optimization algorithm, and F(x(t)) is minimized under the constraints. The evolution will start from the first generation, and the particle position information of the first generation at time t is used as input;

[0027] ② Divide the particles into different groups according to the skill factor and sort the particles by fitness;

[0028] ③ Calculate the degree of constraint violation as

[0029]

[0030] Among them, G1(x N,t (τ)) is the constraint violation degree of the denitrification task at the τth iteration at time t, G2(x P,t (τ)) is the constraint violation degree of the phosphorus removal task at the τth iteration at time t,

[0031] Design the adaptive penalty function of the i-th particle as

[0032]

[0033] Among them, P k (x ik,t (τ)) is the penalty function of the i-th particle of the k-th task in the τ-th iteration at time t, k = 1 or 2, x ik,t (τ) is the position of the i-th particle in the k-th task at the τ-th iteration at time t, i = 1, 2, ..., 100, β t (τ) is the probability of a feasible solution at the τth iteration at time t, which is the number of feasible solutions divided by the total number;

[0034] Calculate the fitness value of the i-th particle as

[0035]

[0036] in, is the fitness value of the i-th particle in the k-th task at the τ-th iteration at time t;

[0037] The particle velocity update formula is:

[0038]

[0039] Among them, v ik,t (τ) is the velocity of the i-th particle of the k-th task in the τ-th iteration at time t, p ik,t (τ) is the individual optimal position of the i-th particle in the k-th task at the τ-th iteration at time t, p gk,t (τ) is the global optimal position of the kth task at the τth iteration at time t, is the knowledge transfer item of the kth task in the τth iteration at time t, r1 is the individual experience random vector whose elements are in the range of [0, 1], r2 is the social experience random vector whose elements are in the range of [0, 1], and r3 is the knowledge transfer item random vector whose elements are in the range of [0, 1];

[0040] The particle position update formula is:

[0041]

[0042] Among them, x ik,t (τ+1) is the position of the i-th particle of the k-th task at the τ+1-th iteration at time t;

[0043] ④ The optimal position of the individual at the kth task of the τth iteration at time t and the archive Θ of the kth task in the τ-1th iteration at time t k,t (τ-1) for comparison, Θ k,t (τ-1)=[Θ k,1,t (τ-1), Θ k,2,t (τ-1),…,Θ k,n,t (τ-1),…,Θ k,100,t (τ-1)],Θ k,n,t (τ-1) is the nth optimal solution in the archive at the τ-1th iteration at time t, n = 1, 2, ..., 100; F(Θ k,n,t (τ-1)) is Θ k,n,t The fitness vector of (τ-1), yes The fitness vector of Then Save to the archive and generate a new τth generation archive Θ k,t (τ); if Then the τth generation archive Θ k,t (τ) and the τ-1 generation archive Θ k,t(τ-1) is the same;

[0044] ⑤ Determine whether to stop iteration: If the current number of iterations τ ≥ 1000, terminate the iteration process and go to step ⑥; otherwise, increase the number of iterations τ by 1 and return to step ②;

[0045] ⑥In the archives Θ 1,t (1000) and Θ 2,t (1000) selects the solution with the same first two dimensions as the optimal setting value at time t, is the optimal setting value of the denitrification task at time t, is the optimal setting value of the phosphorus removal task at time t, is the optimal set value of dissolved oxygen at time t, is the optimal setting value of nitrate nitrogen at time t, is the set value of methanol flow rate at time t, is the polyaluminium chloride flow setting value;

[0046] (3) Multi-task optimization set point tracking control of sewage treatment process

[0047] Design a controller to track and control the optimal set values ​​of denitrification and phosphorus removal tasks, adjust the dissolved oxygen transfer coefficient, internal reflux, methanol dosing pump valve and polyaluminium chloride dosing pump valve, and realize multi-task constrained optimization control of biological denitrification and phosphorus removal in sewage treatment process; Design a controller to track and control the optimal set values ​​of multiple tasks:

[0048]

[0049] Where, Δu(t)=[ΔQ a (t), ΔK L a(t),ΔK F (t), ΔK P (t)] T is the matrix of operational variables, ΔQ a (t) is the change in the internal circulation flow of sewage treatment, ΔK L a(t) is the change in oxygen transfer coefficient of the fifth zone, ΔK F (t) is the change in the valve opening of the methanol dosing pump, ΔK P (t) is the change in the valve opening of the polyaluminium chloride dosing pump; e(t) = u * (t)-u(t) is the control error at time t, is the optimal setting value at time t u(t) = [S NO (t), S O (t), M F (t), M P (t)] Tis the actual output value at time t;

[0050] Adjust the dissolved oxygen transfer coefficient, internal reflux flow, methanol dosing pump valve opening and polyaluminium chloride dosing pump valve opening:

[0051] K L a(t+1)=K L a(t)+ΔK L a(t) (13)

[0052] Q a (t+1)=Q a (t)+ΔQ a (t) (14)

[0053] K F (t+1)=K F (t)+ΔK F (t) (15)

[0054] K P (t+1)=K P (t)+ΔK P (t) (16)

[0055] Among them, K L a(t) is the dissolved oxygen transfer coefficient at time t, Q a (t) is the internal recirculation flow at time t, K F (t) is the valve opening of the methanol dosing pump at time t, K P (t) is the valve opening of the polyaluminium chloride dosing pump at time t; the frequency of the oxygen supply pump and the reflux pump is adjusted by the frequency converter, and the valve opening of the methanol and polyaluminium chloride dosing pumps is adjusted by the potentiometer of the electronic valve, then the dissolved oxygen concentration will be adjusted to The nitrate nitrogen concentration will be adjusted to The methanol flow rate will be adjusted to The polyaluminium chloride flow rate will be adjusted to At this point, multi-task constrained optimization control of biological nitrogen removal and phosphorus removal in the sewage treatment process has been achieved.

[0056] The creativity of the present invention is mainly reflected in:

[0057] (1) The present invention aims at the multi-task constrained optimization control problem of biological denitrification and phosphorus removal in sewage treatment process, constructs a multi-task constrained optimization model of sewage treatment process, designs a multi-task optimization control method of sewage treatment process based on adaptive penalty function, obtains the optimized set values ​​of dissolved oxygen, nitrate nitrogen, methanol flow rate and polyaluminium chloride flow rate and performs tracking control. The method realizes the constrained optimization of denitrification task and phosphorus removal task, and completes the multi-task constrained optimization control of sewage treatment process.

[0058] (2) The present invention designs a multi-task constrained optimization control method for a sewage treatment process based on an adaptive penalty function to optimize the control of dissolved oxygen, nitrate nitrogen, methanol flow and polyaluminium chloride flow in the sewage treatment process. The method simultaneously considers the fitness value and the degree of constraint violation, takes the constraint conditions into account in the target, promotes the parallel optimization of denitrification tasks and phosphorus removal tasks, and then obtains the optimized set value of the sewage treatment process, and designs a multi-task controller to track and control the optimized set value to obtain a better control effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is the dissolved oxygen tracking control result diagram of the optimization control method of the present invention

[0060] Figure 2 This is the nitrate nitrogen tracking control result diagram of the optimization control method of the present invention

[0061] Figure 3 This is the methanol flow tracking control result diagram of the optimization control method of the present invention

[0062] Figure 4 This is the polyaluminium chloride flow tracking control result diagram of the optimization control method of the present invention DETAILED DESCRIPTION

[0063] The present invention adopts the following technical solutions and implementation steps:

[0064] 1. A sewage treatment process optimization control method based on multi-task constraints, characterized in that a sewage treatment process optimization target model with multi-task constraints is established, a sewage treatment process multi-task constraint optimization setting method based on an adaptive penalty function is studied, and a controller is designed to implement tracking control of multi-task optimization setting values, thereby promoting the parallel optimization of biological denitrification tasks and biological phosphorus removal tasks in the sewage treatment process, comprising the following steps:

[0065] (1) Establishing a multi-task constrained sewage treatment process optimization target model

[0066] The multi-task constrained optimization target model of the sewage treatment process describes the optimization targets of the biological denitrification task and the biological phosphorus removal task through an adaptive kernel function; the constrained optimization target model of the biological denitrification task is

[0067]

[0068] Among them, f1(x N (t)) is the constrained optimization target energy consumption model of the biological denitrification task in the sewage treatment process at time t, x N (t) = [S O (t), S NO (t), M F (t), SNH (t)] is the input variable of the denitrification task optimization model at time t, Ω N is the decision space of the optimization target model of denitrification task, S O (t) is the aerobic terminal dissolved oxygen concentration at time t and its value range is [0.4, 3], in mg / L, S O (0) = 1.5 mg / L, S NO (t) is the anaerobic terminal nitrate nitrogen concentration at time t and its value range is [0.3, 2], unit is mg / L, S NO (0) = 1 mg / L, M F (t) is the methanol flow rate at time t and its value range is [60, 400], unit is liter / hour, M F (0) = 100 liters / hour, S NH (t) is the effluent ammonia nitrogen concentration at time t and its value range is [0, 2.5], unit is mg / L, S NH (0) = 2.3 mg / L, a1(t) is the output offset of the energy consumption model of the denitrification task in the sewage treatment process at time t and its value range is [-2, 2], a1(0) = -1.21, W 1,a (t) is the weight of the ath adaptive kernel function of the denitrification task energy consumption model of the sewage treatment process at time t and its value range is [-3, 3], W 1,a (0) = 1.8, c 1,a (t) = [c 1,a,1 (t), c 1,a,2 (t), c 1,a,3 (t), c 1,a,4 (t)] T is the center of the ath adaptive kernel function of the denitrification task energy consumption model of the sewage treatment process at time t, and its element value range is [-1, 1], c 1,a (0) = [0.68, 0.51, 0.34, 0.21] T , T is the transpose of a vector or matrix, σ 1,a (t) is the width of the ath adaptive kernel function of the denitrification task energy consumption model at time t and its value range is [0, 2], σ 1,a (0) = 1.69; f N (x N (t)) is the constraint condition of the denitrification task of the sewage treatment process at time t, and the total nitrogen in the effluent

[0069]

[0070] Where a2(t) is the output offset of the total nitrogen model of the sewage treatment process denitrification task at time t and its value range is [-2, 2], a2(0) = -0.97, W 2,a(t) is the weight of the ath adaptive kernel function of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t and its value range is [-3, 3], W 2,a (0) = 1.51, c 2,a (t) = [c 2,a,1 (t), c 2,a,2 (t), c 2,a,3 (t), c 2,a,4 (t)] T is the center of the ath adaptive kernel function of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t, and its element value range is [-1, 1], c 2,a (0) = [-0.27, 0.75, 0.34, 0.87] T , σ 2,a (t) is the width of the ath adaptive kernel function of the total nitrogen model of the denitrification task effluent at time t and its value range is [0, 2], σ 2,a (0) = 0.64;

[0071] Parameter update of denitrification task constraint optimization model in sewage treatment process:

[0072]

[0073] Where A(t) = [a1(t), a2(t), W 1,a (t), W 2,a (t), c 1,a,1 (t), c 1,a,2 (t), c 1,a,3 (t), c 1,a,4 (t), c 2,a,1 (t), c 2,a,2 (t), c 2,a,3 (t), c 2,a,4 (t),σ 1,a (t),σ 2,a (t)] is the parameter of the denitrification task constraint optimization model of the sewage treatment process at time t; α1 is the learning rate of the parameter of the denitrification task constraint optimization model of the sewage treatment process and its value range is [0, 1]; e 1u (t) = y1(t) - y 1u (t) is the prediction error of the denitrification task constraint optimization model of the sewage treatment process at time t, y1(t) is the output of the denitrification task constraint optimization model of the sewage treatment process at time t, y 1u (t) = [EC N (t), TN(t)] is the actual output value of the denitrification task of the sewage treatment process at time t, EC(t) is the actual energy consumption value of the denitrification task of the sewage treatment process at time t, and TN(t) is the actual effluent total nitrogen value of the sewage treatment process at time t;

[0074] The constrained optimization objective model of biological phosphorus removal task is

[0075]

[0076] Among them, f2(x P (t)) is the constrained optimization target energy consumption model of biological phosphorus removal task in sewage treatment process at time t, x P (t) = [S O (t), S NO (t), M P (t), MLSS(t)] is the input variable of the optimization model for phosphorus removal task at time t, Ω P is the decision space of the optimization target model for phosphorus removal task, M P (t) is the polyaluminium chloride flow rate at time t and its value range is [0, 108], unit is liter / hour, M P (0) = 50 L / h, MLSS(t) is the effluent mixed suspended solids concentration at time t and the value range is [0, 100], unit is mg / L, MLSS(0) = 15 mg / L, b1(t) is the output offset of the energy consumption model of the phosphorus removal task in the sewage treatment process at time t and the value range is [-2, 2], b1(0) = -0.97, W 1,b (t) is the weight of the bth adaptive kernel function of the energy consumption model of phosphorus removal task in sewage treatment process at time t and its value range is [-3, 3], W 1,b (0) = 1.75, c 1,b (t) = [c 1,b,1 (t), c 1,b,2 (t), c 1,b,3 (t), c 1,b,4 (t)] T is the center of the bth adaptive kernel function of the energy consumption model of phosphorus removal task in sewage treatment process at time t, and its element value range is [-1, 1], c 1,b (0) = [0.74, 0.58, -0.84, -0.42] T , σ 1,b (t) is the width of the bth adaptive kernel function of the energy consumption model of the phosphorus removal task at time t and its value range is [0, 2], σ 1,b (0) = 1.52; f TP (x P (t)) is the constraint condition of the phosphorus removal task in the sewage treatment process at time t, the total phosphorus in the effluent

[0077]

[0078] Where b2(t) is the output offset of the total phosphorus model of the effluent of the sewage treatment process at time t and its value range is [-2, 2], b2(0) = 1.23, W 2,b (t) is the weight of the bth adaptive kernel function of the effluent total phosphorus model of the sewage treatment process for phosphorus removal task at time t and its value range is [-3, 3], W 2,b (0) = 2.18, c 2,b (t) = [c 2,b,1 (t), c 2,b,2 (t), c 2,b,3 (t), c 2,b,4 (t)] T is the center of the bth adaptive kernel function of the effluent total phosphorus model of the sewage treatment process for phosphorus removal task at time t, and its element value range is [-1, 1], c 2,b (0) = [0.41, -0.86, 0.44, -0.25] T , σ 2,b (t) is the width of the bth adaptive kernel function of the total phosphorus model of the phosphorus removal task at time t and its value range is [0, 2], σ 2,b (0) = 0.95;

[0079] Parameter update of phosphorus removal task constraint optimization model in sewage treatment process:

[0080]

[0081] Where, B(t) = [b1(t), b2(t), W 1,b (t), W 2,b (t), c 1,b,1 (t), c 1,b,2 (t), c 1,b,3 (t), c 1,b,4 (t), c 2,b,1 (t), c 2,b,2 (t), c 2,b,3 (t), c 2,b,4 (t),σ 1,b (t),σ 2,b (t)] is the parameter of the optimization model for phosphorus removal task constraints in the sewage treatment process at time t; α2 is the learning rate of the optimization model for phosphorus removal task constraints in the sewage treatment process and its value range is [0, 1]; e 2u (t) = y2(t) - y 2u (t) is the prediction error of the optimization model for phosphorus removal task constraints in the sewage treatment process at time t, y2(t) is the output of the optimization model for phosphorus removal task constraints in the sewage treatment process at time t, y 2u (t) = [EC P(t), TP(t)] is the actual output value of the phosphorus removal task of the sewage treatment process at time t, EC(t) is the actual energy consumption value of the phosphorus removal task of the sewage treatment process at time t, and TP(t) is the actual effluent total phosphorus value of the sewage treatment process at time t;

[0082] (2) Solving the optimal setpoints for the sewage treatment process with multi-task constraints

[0083] Design a constrained multi-task particle swarm algorithm based on adaptive penalty function to solve the optimal set value of the sewage treatment process, taking into account the fitness value and constraint violation degree, and obtain the multi-task optimal set value;

[0084] ① Set the particle swarm size of each task in the multi-task particle swarm optimization process to 100, the number of tasks to 2, the total number of iterations to 1000, and initialize the external archive Θ t (0) is an empty set, and the position, velocity and skill factor of the particle are randomly initialized. The multi-task constrained optimization target model of the sewage treatment process is F(x(t))=[f1(x N (t)), f2(x P (t))] is used as the optimization objective of the multi-task particle swarm optimization algorithm, and F(x(t)) is minimized under the constraints. The evolution will start from the first generation, and the particle position information of the first generation at time t is used as input;

[0085] ② Divide the particles into different groups according to the skill factor and sort the particles by fitness;

[0086] ③ Calculate the degree of constraint violation as

[0087]

[0088] Among them, G1(x N,t (τ)) is the constraint violation degree of the denitrification task at the τth iteration at time t, G2(x P,t (τ)) is the constraint violation degree of the phosphorus removal task at the τth iteration at time t,

[0089] Design the adaptive penalty function of the i-th particle as

[0090]

[0091] Among them, P k (x ik,t (τ)) is the penalty function of the i-th particle of the k-th task in the τ-th iteration at time t, k = 1 or 2, x ik,t (τ) is the position of the i-th particle in the k-th task at the τ-th iteration at time t, i = 1, 2, ..., 100, β t (τ) is the probability of a feasible solution at the τth iteration at time t, which is the number of feasible solutions divided by the total number;

[0092] Calculate the fitness value of the i-th particle as

[0093]

[0094] in, is the fitness value of the i-th particle in the k-th task at the τ-th iteration at time t;

[0095] The particle velocity update formula is:

[0096]

[0097] Among them, v ik,t (τ) is the velocity of the i-th particle of the k-th task in the τ-th iteration at time t, p ik,t (τ) is the individual optimal position of the i-th particle in the k-th task at the τ-th iteration at time t, p gk,t (τ) is the global optimal position of the kth task at the τth iteration at time t, is the knowledge transfer item of the kth task in the τth iteration at time t, r1 is the individual experience random vector whose elements are in the range of [0, 1], r2 is the social experience random vector whose elements are in the range of [0, 1], and r3 is the knowledge transfer item random vector whose elements are in the range of [0, 1];

[0098] The particle position update formula is:

[0099]

[0100] Among them, x ik,t (τ+1) is the position of the i-th particle of the k-th task at the τ+1-th iteration at time t;

[0101] ④ The optimal position of the individual at the kth task of the τth iteration at time t and the archive Θ of the kth task in the τ-1th iteration at time t k,t (τ-1) for comparison, Θ k,t (τ-1) = [Θ k,1,t (τ-1), Θ k,2,t (τ-1),…,Θ k,n,t (τ-1),…,Θ k,100,t (τ-1)],Θ k,n,t (τ-1) is the nth optimal solution in the archive at the τ-1th iteration at time t, n = 1, 2, ..., 100; F(Θ k,n,t (τ-1)) is Θ k,n,t The fitness vector of (τ-1), yes The fitness vector of Then Save to the archive and generate a new τth generation archive Θ k,t (τ); if Then the τth generation archive Θ k,t (τ) and the τ-1 generation archive Θ k,t (τ-1) is the same;

[0102] ⑤ Determine whether to stop iteration: If the current number of iterations τ ≥ 1000, terminate the iteration process and go to step ⑥; otherwise, increase the number of iterations τ by 1 and return to step ②;

[0103] ⑥In the archives Θ 1,t (1000) and Θ 2,t (1000) selects the solution with the same first two dimensions as the optimal setting value at time t, is the optimal setting value of the denitrification task at time t, is the optimal setting value of the phosphorus removal task at time t, is the optimal set value of dissolved oxygen at time t, is the optimal setting value of nitrate nitrogen at time t, is the set value of methanol flow rate at time t, is the polyaluminium chloride flow setting value;

[0104] (3) Multi-task optimization set point tracking control of sewage treatment process

[0105] Design a controller to track and control the optimal set values ​​of denitrification and phosphorus removal tasks, adjust the dissolved oxygen transfer coefficient, internal reflux, methanol dosing pump valve and polyaluminium chloride dosing pump valve, and realize multi-task constrained optimization control of biological denitrification and phosphorus removal in sewage treatment process; Design a controller to track and control the optimal set values ​​of multiple tasks:

[0106]

[0107] Where, Δu(t)=[ΔQ a (t), ΔK L a(t),ΔK F (t), ΔK P (t)] T is the matrix of operational variables, ΔQ a (t) is the change in the internal circulation flow of sewage treatment, ΔK L a(t) is the change in oxygen transfer coefficient of the fifth zone, ΔK F (t) is the change in the valve opening of the methanol dosing pump, ΔK P(t) is the change in the valve opening of the polyaluminium chloride dosing pump; e(t) = u * (t)-u(t) is the control error at time t, is the optimal setting value at time t, u(t)=[S NO (t), S O (t), M F (t), M P (t)] T is the actual output value at time t;

[0108] Adjust the dissolved oxygen transfer coefficient, internal reflux flow, methanol dosing pump valve opening and polyaluminium chloride dosing pump valve opening:

[0109] K L a(t+1)=K L a(t)+ΔK L a(t) (29)

[0110] Q a (t+1)=Q a (t)+ΔQ a (t) (30)

[0111] K F (t+1)=K F (t)+ΔK F (t) (31)

[0112] K P (t+1)=K P (t)+ΔK P (t) (32)

[0113] Among them, K L a(t) is the dissolved oxygen transfer coefficient at time t, Q a (t) is the internal recirculation flow at time t, K F (t) is the valve opening of the methanol dosing pump at time t, K P (t) is the valve opening of the polyaluminium chloride dosing pump at time t; the frequency of the oxygen supply pump and the reflux pump is adjusted by the frequency converter, and the valve opening of the methanol and polyaluminium chloride dosing pumps is adjusted by the potentiometer of the electronic valve, then the dissolved oxygen concentration will be adjusted to The nitrate nitrogen concentration will be adjusted to The methanol flow rate will be adjusted to The polyaluminium chloride flow rate will be adjusted to At this point, multi-task constrained optimization control of biological nitrogen removal and phosphorus removal in the sewage treatment process has been achieved.

[0114] The output results of a sewage treatment process optimization control system based on multi-task constraints are dissolved oxygen concentration, nitrate nitrogen concentration, methanol flow rate and polyaluminium chloride flow rate. Figure 1 It is the dissolved oxygen tracking control result diagram, where the solid line is the optimized set value, the dotted line is the actual output value, the horizontal axis is time, unit: day, the vertical axis is dissolved oxygen concentration, unit: mg / L; Figure 2 The nitrate nitrogen tracking control result diagram, where the solid line is the optimized set value, the dotted line is the actual output value, the horizontal axis: time, unit: day, the vertical axis: nitrate nitrogen concentration, unit: mg / L; Figure 3 This is the methanol flow tracking control result diagram, where the solid line is the optimized set value, the dotted line is the actual output value, the horizontal axis is time, unit: day, the vertical axis is methanol flow, unit: liter / hour; Figure 4 The result diagram of polyaluminium chloride flow tracking control, where the solid line is the optimized set value, the dotted line is the actual output value, the horizontal axis: time, unit: day, the vertical axis: polyaluminium chloride flow, unit: liter / hour; the experimental results show the effectiveness of the sewage treatment process optimization control method based on multi-task constraints.

Claims

1. A sewage treatment process optimization control method based on multi-task constraints, characterized in that: Establish a multi-task constrained sewage treatment process optimization target model, study the multi-task constraint optimization setting method of the sewage treatment process based on the adaptive penalty function, and design a controller to achieve the tracking control of the multi-task optimization setting value, so as to promote the parallel optimization of the biological denitrification task and the biological phosphorus removal task in the sewage treatment process, including the following steps: (1) Establishing a multi-task constrained sewage treatment process optimization target model The multi-task constrained optimization target model of the sewage treatment process describes the optimization targets of the biological denitrification task and the biological phosphorus removal task through an adaptive kernel function; the constrained optimization target model of the biological denitrification task is Among them, f1(x N (t)) is the constrained optimization target energy consumption model of the biological denitrification task in the sewage treatment process at time t, x N (t) = [S O (t), S NO (t), M F (t), S NH (t)] is the input variable of the denitrification task optimization model at time t, Ω N is the decision space of the optimization target model of denitrification task, S O (t) is the aerobic terminal dissolved oxygen concentration at time t and its value range is [0.4, 3], in mg / L, S NO (t) is the anaerobic terminal nitrate nitrogen concentration at time t and its value range is [0.3, 2], unit is mg / L, M F (t) is the methanol flow rate at time t and its value range is [60, 400], unit is liter / hour, S NH (t) is the effluent ammonia nitrogen concentration at time t and its value range is [0, 2.5], unit is mg / L, a1(t) is the output offset of the denitrification task energy consumption model of the sewage treatment process at time t and its value range is [-2, 2], W 1,a (t) is the weight of the ath adaptive kernel function of the energy consumption model of the denitrification task in the sewage treatment process at time t and its value range is [-3, 3], c 1,a (t) = [c 1,a,1 (t), c 1,a,2 (t), c 1,a,3 (t), c 1,a,4 (t)] T is the center vector of the ath adaptive kernel function of the denitrification task energy consumption model of the sewage treatment process at time t, c 1,a,1 (t) is the first dimension center of the ath adaptive kernel function of the denitrification task energy consumption model of the sewage treatment process at time t and its value range is [-1, 1], c 1,a,2 (t) is the second dimensional center of the ath adaptive kernel function of the denitrification task energy consumption model of the sewage treatment process at time t and its value range is [-1, 1], c 1,a,3 (t) is the third-dimensional center of the ath adaptive kernel function of the denitrification task energy consumption model of the sewage treatment process at time t and its value range is [-1, 1], c 1,a,4 (t) is the fourth-dimensional center of the ath adaptive kernel function of the denitrification task energy consumption model of the sewage treatment process at time t and its value range is [-1, 1], T is the transpose of the vector or matrix, σ 1,a (t) is the width of the ath adaptive kernel function of the denitrification task energy consumption model at time t and its value range is [0, 2]; f N (x N (t)) is the constraint condition of the denitrification task of the sewage treatment process at time t, and the total nitrogen in the effluent Among them, a2(t) is the output offset of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t and its value range is [-2, 2], W 2,a (t) is the weight of the ath adaptive kernel function of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t and its value range is [-3, 3], c 2,a (t) = [c 2,a,1 (t),c2,a,2(t),c2,a,3(t),c2,a,4(t)] T is the center vector of the ath adaptive kernel function of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t, c2,a,1(t) is the first dimension center of the ath adaptive kernel function of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t and its value range is [-1, 1], c 2,a,2 (t) is the second dimension center of the ath adaptive kernel function of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t, and its value range is [-1, 1], c 2,a,3 (t) is the third-dimensional center of the ath adaptive kernel function of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t, and its value range is [-1, 1], c 2,a,4 (t) is the fourth dimension center of the ath adaptive kernel function of the total nitrogen model of the effluent of the sewage treatment process denitrification task at time t, and its value range is [-1, 1], σ 2,a (t) is the width of the ath adaptive kernel function of the total nitrogen model of the denitrification task effluent at time t and its value range is [0, 2]; Parameter update of denitrification task constraint optimization model in sewage treatment process: Where A(t) = [a1(t), a2(t), W 1,a (t), W 2,a (t), c 1,a,1 (t), c 1,a,2 (t), c1, a, 3(t), c 1,a,4 (t), c 2,a,1 (t), c 2,a,2 (t), c 2,a,3 (t), c 2,a,4 (t),σ 1,a (t),σ 2,a (t)] is the parameter of the denitrification task constraint optimization model of the sewage treatment process at time t; α1 is the learning rate of the parameter of the denitrification task constraint optimization model of the sewage treatment process and its value range is [0, 1]; e 1u (t) = y1(t) - y 1u (t) is the prediction error of the denitrification task constraint optimization model of the sewage treatment process at time t, y1(t) is the output of the denitrification task constraint optimization model of the sewage treatment process at time t, and y 1u (t) = [EC N (t), TN(t)] is the actual output value of the denitrification task of the sewage treatment process at time t, EC(t) is the actual energy consumption value of the denitrification task of the sewage treatment process at time t, and TN(t) is the actual effluent total nitrogen value of the sewage treatment process at time t; The constrained optimization objective model of biological phosphorus removal task is Among them, f2(x P (t)) is the constrained optimization target energy consumption model of biological phosphorus removal task in sewage treatment process at time t, x P (t) = [S O (t), S NO (t), M P (t), MLSS(t)] is the input variable of the optimization model for phosphorus removal task at time t, Ω P is the decision space of the optimization target model for phosphorus removal task, M P (t) is the polyaluminium chloride flow rate at time t and its value range is [0, 108], unit is liter / hour, MLSS(t) is the effluent mixed suspended solids concentration at time t and its value range is [0, 100], unit is mg / L, b1(t) is the output offset of the energy consumption model of the phosphorus removal task in the sewage treatment process at time t and its value range is [-2, 2], W 1,b (t) is the weight of the bth adaptive kernel function of the energy consumption model for phosphorus removal in the sewage treatment process at time t and its value range is [-3, 3], c1,b(t) = [c1,b,1(t), c1,b,2(t), c1,b,3(t), c 1,b,4 (t)] T is the center vector of the bth adaptive kernel function of the energy consumption model of phosphorus removal task in sewage treatment process at time t, c 1,b,1 (t) is the first dimension center of the b-th adaptive kernel function of the energy consumption model for phosphorus removal in the sewage treatment process at time t, and its value range is [-1, 1]. c1,b,2(t) is the second dimension center of the b-th adaptive kernel function of the energy consumption model for phosphorus removal in the sewage treatment process at time t, and its value range is [-1, 1]. 1,b,3 (t) is the third-dimensional center of the b-th adaptive kernel function of the energy consumption model of phosphorus removal task in the sewage treatment process at time t, and its value range is [-1, 1], c 1,b,4 (t) is the fourth dimension center of the bth adaptive kernel function of the energy consumption model of phosphorus removal task in sewage treatment process at time t and its value range is [-1, 1], σ 1,b (t) is the width of the bth adaptive kernel function of the energy consumption model of the phosphorus removal task at time t and its value range is [0, 2]; f TP (x P (t)) is the constraint condition of phosphorus removal task in sewage treatment process at time t, total phosphorus in effluent Where b2(t) is the output offset of the total phosphorus model of the effluent of the sewage treatment process at time t and its value range is [-2, 2], W 2,b (t) is the weight of the bth adaptive kernel function of the total phosphorus model of the effluent of the sewage treatment process at time t and its value range is [-3, 3], c 2,b (t) = [c 2,b,1 (t), c 2,b,2 (t), c 2,b,3 (t), c 2,b,4 (t)] T is the center vector of the bth adaptive kernel function of the effluent total phosphorus model of the sewage treatment process for phosphorus removal task at time t, c 1,b,1 (t) is the first dimension center of the bth adaptive kernel function of the total phosphorus model of the effluent of the sewage treatment process for phosphorus removal task at time t, and its value range is [-1, 1], c 1,b,2 (t) is the second dimension center of the b-th adaptive kernel function of the total phosphorus model of the effluent of the sewage treatment process at time t, and its value range is [-1, 1], c 1,b,3 (t) is the third-dimensional center of the b-th adaptive kernel function of the total phosphorus model of the effluent of the sewage treatment process for phosphorus removal task at time t, and its value range is [-1, 1], c 1,b,4 (t) is the fourth dimension center of the b-th adaptive kernel function of the total phosphorus model of the effluent of the sewage treatment process at time t, and its value range is [-1, 1], σ 2,b (t) is the width of the bth adaptive kernel function of the total phosphorus model of the phosphorus removal task effluent at time t and its value range is [0, 2]; Parameter update of phosphorus removal task constraint optimization model in sewage treatment process: Where, B(t) = [b1(t), b2(t), W 1,b (t), W 2,b (t), c 1,b,1 (t), c 1,b,2 (t), c 1,b,3 (t), c 1,b,4 (t), c 2,b,1 (t), c 2,b,2 (t), c 2,b,3 (t), c 2,b,4 (t),σ 1,b (t),σ 2,b (t)] is the parameter of the optimization model for phosphorus removal task constraints in the sewage treatment process at time t; α2 is the learning rate of the optimization model for phosphorus removal task constraints in the sewage treatment process and its value range is [0, 1]; e 2u (t) = y2(t) - y 2u (t) is the prediction error of the optimization model for phosphorus removal task constraints in the sewage treatment process at time t, y2(t) is the output of the optimization model for phosphorus removal task constraints in the sewage treatment process at time t, and y 2u (t) = [EC P (t), TP(t)] is the actual output value of the phosphorus removal task of the sewage treatment process at time t, EC(t) is the actual energy consumption value of the phosphorus removal task of the sewage treatment process at time t, and TP(t) is the actual effluent total phosphorus value of the sewage treatment process at time t; (2) Solving the optimal setpoints for the sewage treatment process with multi-task constraints Design a constrained multi-task particle swarm algorithm based on adaptive penalty function to solve the optimal set value of the sewage treatment process, taking into account the fitness value and constraint violation degree, and obtain the multi-task optimal set value; ① Set the particle swarm size of each task in the multi-task particle swarm optimization process to 100, the number of tasks to 2, the total number of iterations to 1000, and initialize the external archive Θ t (0) is an empty set, and the position, velocity and skill factor of the particle are randomly initialized. The multi-task constrained optimization target model of the sewage treatment process is F(x(t))=[f1(x N (t)), f2(x P (t))] is used as the optimization objective of the multi-task particle swarm optimization algorithm, and F(x(t)) is minimized under the constraints. The evolution will start from the first generation, and the particle position information of the first generation at time t is used as input; ② Divide the particles into different groups according to the skill factor and sort the particles by fitness; ③ Calculate the degree of constraint violation as Among them, G1(x N,t (τ)) is the constraint violation degree of the denitrification task at the τth iteration at time t, G2(x P,t (τ)) is the constraint violation degree of the phosphorus removal task at the τth iteration at time t, Design the adaptive penalty function of the i-th particle as in, is the penalty function of the i-th particle of the k-th task in the τ-th iteration at time t, k = 1 or 2, is the position of the i-th particle in the k-th task at the τ-th iteration at time t, i = 1, 2, ..., 100, β t (τ) is the probability of a feasible solution at the τth iteration at time t, which is the number of feasible solutions divided by the total number; Calculate the fitness value of the i-th particle as in, is the fitness value of the i-th particle in the k-th task at the τ-th iteration at time t; The particle velocity update formula is: in, is the velocity of the i-th particle of the k-th task in the τ-th iteration at time t, is the individual optimal position of the i-th particle in the k-th task at the τ-th iteration at time t, is the global optimal position of the kth task at the τth iteration at time t, is the knowledge transfer item of the kth task in the τth iteration at time t, r1 is the individual experience random vector whose elements are in the range of [0, 1], r2 is the social experience random vector whose elements are in the range of [0, 1], and r3 is the knowledge transfer item random vector whose elements are in the range of [0, 1]; The particle position update formula is: in, is the position of the i-th particle of the k-th task in the τ+1-th iteration at time t; ④ The optimal position of the individual at the kth task of the τth iteration at time t and the archive Θ of the kth task at the τ-1th iteration at time t k,t (τ-1) for comparison, Θ k,t (τ-1)=[Θ k,1,t (τ-1), Θ k,2,t (τ-1),…,Θ k,n,t (τ-1),…,Θ k,100,t (τ-1)],Θ k,n,t (τ-1) is the nth optimal solution in the archive at the τ-1th iteration at time t, n = 1, 2, ..., 100; F(Θ k,n,t (τ-1)) is Θ k,n,t The fitness vector of (τ-1), yes The fitness vector of Then Save to the archive and generate a new τth generation archive Θ k,t (τ); if Then the τth generation archive Θ k,t (τ) and the τ-1 generation archive Θ k,t (τ-1) is the same; ⑤ Determine whether to stop iteration: If the current number of iterations τ ≥ 1000, terminate the iteration process and go to step ⑥; otherwise, increase the number of iterations τ by 1 and return to step ②; ⑥In the archives Θ 1,t (1000) and Θ 2,t (1000) selects the solution with the same first two dimensions as the optimal setting value at time t, is the optimal setting value of the denitrification task at time t, is the optimal setting value of the phosphorus removal task at time t, is the optimal set value of dissolved oxygen at time t, is the optimal setting value of nitrate nitrogen at time t, is the set value of methanol flow rate at time t, is the polyaluminium chloride flow setting value; (3) Multi-task optimization set point tracking control in sewage treatment process Design a controller to track and control the optimal set values ​​of denitrification and phosphorus removal tasks, adjust the dissolved oxygen transfer coefficient, internal reflux, methanol dosing pump valve and polyaluminium chloride dosing pump valve, and realize multi-task constrained optimization control of biological denitrification and phosphorus removal in sewage treatment process; Design a controller to track and control the optimal set values ​​of multiple tasks: Where, Δu(t)=[ΔQ a (t), ΔK L a(t),ΔK F (t), ΔK P (t)] T is the matrix of operational variables, ΔQ a (t) is the change in the internal circulation flow of sewage treatment, ΔK L a(t) is the change in oxygen transfer coefficient of the fifth zone, ΔK F (t) is the change in the valve opening of the methanol dosing pump, ΔK P (t) is the change in the valve opening of the polyaluminium chloride dosing pump; e(t) = u * (t)-u(t) is the control error at time t, is the optimal setting value at time t u(t) = [S NO (t), S O (t), M F (t), M P (t)] T is the actual output value at time t; Adjust the dissolved oxygen transfer coefficient, internal reflux flow, methanol dosing pump valve opening and polyaluminium chloride dosing pump valve opening: K L a(t+1)=K L a(t)+ΔK L a(t) (13) Q a (t+1)=Q a (t)+ΔQ a (t) (14) K F (t+1)=K F (t)+ΔK F (t) (15) K P (t+1)=K P (t)+ΔK P (t) (16) Among them, K L a(t) is the dissolved oxygen transfer coefficient at time t, Q a (t) is the internal recirculation flow at time t, K F (t) is the valve opening of the methanol dosing pump at time t, K P (t) is the valve opening of the polyaluminium chloride dosing pump at time t; the frequency of the oxygen supply pump and the reflux pump is adjusted by the frequency converter, and the valve opening of the methanol and polyaluminium chloride dosing pumps is adjusted by the potentiometer of the electronic valve, then the dissolved oxygen concentration will be adjusted to The nitrate nitrogen concentration will be adjusted to The methanol flow rate will be adjusted to The polyaluminium chloride flow rate will be adjusted to At this point, multi-task constrained optimization control of biological nitrogen removal and phosphorus removal in the sewage treatment process has been achieved.

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