A linear active disturbance rejection control-based sodium hypochlorite dosing method

Abstract

The application discloses a sodium hypochlorite dosing method based on linear active disturbance rejection control, and comprises the following steps: S1, data acquisition, collecting water inflow, dosing amount, residual chlorine and water temperature data as basic data sources for subsequent control processes; S2, system modeling, using the seagull optimization algorithm to fit the water purification process system into a first-order transfer function model; S3, parameter calculation, calculating the bandwidth omega of the linear state error feedback control law according to the bandwidth method c and the extended state observer bandwidth omega o , calculating system coefficients b0, extended state observer feedback gains beta1 and beta2; S4, system testing and optimization, testing the control response speed and stability of the system, and optimizing the control parameters according to actual production requirements. Through the optimization control strategy, the application overcomes the limitations of the existing control mode, realizes the precise control of the sodium hypochlorite dosing amount, improves the utilization efficiency of sodium hypochlorite in the water purification process, ensures that the residual chlorine of the discharged water is stable and up to the standard, and reduces the operation cost of the water plant and the water quality risk.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water treatment, in particular to a sodium hypochlorite dosing method based on linear active disturbance rejection control. BACKGROUND

[0002] Currently, the post-filter chlorination in water treatment plants generally adopts a control mode of flow or residual chlorine feedback. However, the flow feedback control mode cannot accurately control the residual chlorine value, and often needs manual intervention; the residual chlorine feedback PID control mode cannot solve the problems of feedback delay and environmental factor changes, resulting in the problem that the sodium hypochlorite dosing amount cannot keep up with the change of residual chlorine in water in real time. The limitations of these control modes increase the risk of exceeding the residual chlorine standard of finished water in water plants, and also increase the amount of chlorine used and energy consumption in water plants.

[0003] Linear active disturbance rejection control (LADRC) is an advanced control technology that does not rely on the accurate mathematical model of the controlled process, has strong disturbance suppression capability, and is suitable for systems with large time delay, random uncertainty, time-varying, and multi-variable coupling. The principle of linear active disturbance rejection control is shown in FIG. 1, wherein ref LSEF is a linear state error feedback control law, LESO is an extended state observer, b0 is a constant parameter, and the control principle of the sodium hypochlorite dosing system is shown in FIG. 2, wherein u is the dosing amount, and y is the residual chlorine of the finished water. Figure 2 SUMMARY

[0004] The technical problem to be solved by the present application is to provide a sodium hypochlorite dosing method based on linear active disturbance rejection control, which overcomes the limitations of existing control modes by optimizing the control strategy, realizes accurate control of the sodium hypochlorite dosing amount, improves the utilization efficiency of sodium hypochlorite in water purification process, ensures that the residual chlorine of the finished water meets the standard stably, and reduces the operation cost and water quality risk of water plants.

[0005] To solve the above technical problems, the technical solution adopted by the present application is:

[0006] A sodium hypochlorite dosing method based on linear active disturbance rejection control, characterized in that it comprises the following steps:

[0007] S1, data acquisition, acquiring the water inflow, dosing amount, residual chlorine, and water temperature data as the basic data source for the subsequent control process;

[0008] S2, system modeling, using the seagull optimization algorithm to fit the water purification process system into a first-order transfer function model;

[0009] S3, parameter calculation, calculating the bandwidth ω of the linear state error feedback control law according to the bandwidth method​c and the extended state observer bandwidth ω o , calculate system coefficients b0, extended state observer feedback gain β1 and β2;

[0010] S4, system test and optimization, test the system control response speed and stability, and optimize the control parameters according to the actual production requirements.

[0011] Preferably, the step S2 is specifically as follows:

[0012] Simplify the water purification process system as a first-order transfer function as a control object, and design a linear state error feedback control law and an extended state observer, wherein,

[0013] The extended state observer is designed as:

[0014] ;

[0015] In the formula, , , , ;

[0016] Then use the seagull algorithm to fit the control object into a first-order transfer function form.

[0017] Preferably, the seagull algorithm comprises: (1) migration behavior

[0018] In order to avoid collision between each seagull and the surrounding seagulls, the position of the seagull is adjusted through the variable A;

[0019] ;

[0020] In the formula: C S (t) is the new position without collision with other seagulls; P S (t) is the current position of the seagull; A is the movement behavior of the seagull; t is the current iteration number; the size of A is controlled by fc;

[0021] ;

[0022] Wherein: Kmax is the maximum iteration number; fc value is 2, and then all seagulls are allowed to approach the best seagull position;

[0023] ;

[0024] ;

[0025] Wherein: M S (t) is the direction of the best seagull; P S(t) is the position of the best albatross; B is an important random parameter responsible for the balance of global and local search algorithm, rand is a random number in the range of [0, 1]; the albatross moves towards the position where the best albatross is located, and reaches a new position D S (t);

[0026] ;

[0027] (2) attack behavior

[0028] When attacking the prey, the albatross performs a spiral-shaped movement in the air; the attack position of the albatross is:

[0029] ;

[0030] Wherein: r is the spiral radius; k is a random angle value, and the value is between 0 and 2π; u and v are related constants of the spiral shape; e is the base of natural logarithm;

[0031] The amount of medicine in the collected n data is set as the system input u, and the residual chlorine of the factory water is set as the system output y, the fitting function output is f, and the fitting target function is set as:

[0032] ;

[0033] ;

[0034] The transfer function is set as s0, T as a variable, the albatross algorithm is used for optimization, and the optimal fitting parameters are obtained.

[0035] Preferably, in the step S3, the expected response time is 300s, the system controller bandwidth , then

[0036] .

[0037] Preferably, in the step S4, in the system test and optimization step, if the residual chlorine does not reach the given value within the specified time (such as 10min), or the residual chlorine fluctuation exceeds the predetermined stable range (such as ±0.1mg / L), then by adjusting ω c And other related control parameters, re-run the system and test until the residual chlorine control precision and stability requirements are met, and the optimal running state of the system is realized.

[0038] The present application provides a linear active disturbance rejection control based sodium hypochlorite dosing method, which has the following beneficial effects:

[0039] 1. Effectively solve the feedback time lag problem of sodium hypochlorite solution in water purification process, can quickly respond to system disturbance, enhance the robustness of the system, so that the sodium hypochlorite dosage can adapt to water quality changes in time, improve the utilization efficiency of sodium hypochlorite solution, and reduce the waste of reagent;

[0040] 2. Simplify the control object to a first-order linear system, significantly reduce the modeling and calculation difficulty, reduce the dependence on complex mathematical model, improve the practicability and operability of the control method, and facilitate the popularization and application in actual production of water purification plant;

[0041] 3. By accurately controlling the residual chlorine value, the risk of exceeding the residual chlorine of finished water is reduced, the safety of water supply water quality is ensured, and the increase of water plant chlorine consumption and energy consumption caused by excessive residual chlorine or unreasonable dosage is reduced, which helps to reduce the operation cost of water plant, improve the economic benefit and environmental benefit. BRIEF DESCRIPTION OF DRAWINGS

[0042] The application will be further described below in combination with the drawings and examples:

[0043] Figure 1 It is a linear self-disturbance control schematic diagram;

[0044] Figure 2 It is a sodium hypochlorite dosing system control schematic diagram;

[0045] Figure 3 It is a method flowchart of the application;

[0046] Figure 4 It is a schematic diagram of simplifying the water purification process system to a first-order transfer function as a control object of the application;

[0047] Figure 5 It is a fitting effect diagram of the application;

[0048] Figure 6 It is a residual chlorine tracking diagram of the application;

[0049] Figure 7 It is a residual chlorine tracking disturbance estimation diagram of the application;

[0050] Figure 8 It is a dosage and residual chlorine change relationship diagram of the application;

[0051] Figure 9 It is a stability test diagram of the application;

[0052] Figure 10 It is a stability test disturbance estimation diagram of the application. DETAILED DESCRIPTION

[0053] As Figure 3As shown, a linear active disturbance rejection control-based sodium hypochlorite dosing method includes the following steps:

[0054] S1, data acquisition, collecting water inflow, dosing amount, residual chlorine and water temperature data as the basis data source for subsequent control process;

[0055] S2, system modeling, using the seagull optimization algorithm to fit the water purification process system into a first-order transfer function model;

[0056] S3, parameter calculation, according to the bandwidth method to calculate the bandwidth ω of the linear state error feedback control law c and the bandwidth ω of the extended state observer o , calculate the system coefficient b0, the feedback gain β1 and β2 of the extended state observer;

[0057] S4, system test and optimization, test the control response speed and stability of the system, and optimize the control parameters according to the actual production requirements.

[0058] Preferably, the step S2 is specifically as follows:

[0059] The water purification process system is simplified as a first-order transfer function as a control object, and a linear state error feedback control law and an extended state observer are designed, wherein,

[0060] The extended state observer is designed as:

[0061] ;

[0062] In the formula, , , , ;

[0063] The linear state error feedback control law is the same as the traditional PID closed-loop control principle, which can control the system to realize given signal tracking. The extended state observer can quickly estimate the disturbance in the system and actively compensate in the control to offset the influence of the disturbance on the system, thereby improving the anti-interference ability of the system.

[0064] Then use the seagull algorithm to fit the control object into a first-order transfer function form.

[0065] Preferably, the seagull algorithm includes: (1) migration behavior

[0066] In order to avoid collision between each seagull and surrounding seagulls, the position of the seagull is adjusted through variable A;

[0067] ;

[0068] In the formula: C S(t) is a new position that does not collide with other seagulls; P S (t) is the current position of the seagull; A is the motion behavior of the seagull; t is the current iteration number; the size of A is controlled by fc;

[0069] ;

[0070] wherein: Kmax is the maximum iteration number; the value of fc is 2, and then all seagulls move towards the best seagull position;

[0071] ;

[0072] ;

[0073] wherein: M S (t) is the direction of the best seagull; P S (t) is the position of the best seagull; B is an important random parameter responsible for the global and local search of the balance algorithm, rand is a random number in the range of [0, 1]; the seagull moves towards the position of the best seagull and reaches a new position D S (t);

[0074] ;

[0075] (2) Attack behavior

[0076] When attacking prey, the seagull performs a spiral-shaped motion in the air; the attack position of the seagull is:

[0077] ;

[0078] wherein: r is the spiral radius; k is a random angle value and takes a value between 0 and 2π; u and v are related constants of the spiral shape; e is the base of the natural logarithm;

[0079] Set the amount of medicine in the collection of n data as the system input u, and the residual chlorine of the factory water as the system output y. The fitting function output is f, and the fitting objective function is set as:

[0080] ;

[0081] ;

[0082] Set s0 and T in the transfer function as variables, and use the seagull algorithm for optimization to obtain the optimal fitting parameters. Example: the first-order transfer function of the water purification process system is fitted as , and the fitting effect is shown in Figure 5 .

[0083] The linear active disturbance rejection controller bandwidth ωc determines the speed of the controller's response to the system dynamics, affecting the system's transient and steady-state performance. A larger controller bandwidth means that the controller will react faster to high-frequency disturbances, but it can also cause an increase in the oscillation of the system output. The observer bandwidth ωo determines the speed of the extended state observer (ESO) response to disturbance estimation. A larger observer bandwidth can estimate and compensate for disturbances more quickly, but it can also increase the oscillation of the system. The bandwidth method gives the relationship between the parameters and the system bandwidth, simplifying the parameter tuning process of the state observer.

[0084] Preferably, in step S3, the desired response time is 300 s, and the system controller bandwidth ωc is set to 0.1 rad / s. Then

[0085] .

[0086] Preferably, in step S4, during the system testing and optimization step, if the residual chlorine does not reach the given value within the specified time (e.g. 10 min) or the residual chlorine fluctuation exceeds the predetermined stable range (e.g. ±0.1 mg / L), the system is re-run by adjusting ω c and other related control parameters, and the system is tested until the residual chlorine control accuracy and stability requirements are met, achieving the optimal operating state of the system.

[0087] The response speed of the linear active disturbance rejection control system is tested, and from Figure 6 it can be seen that the residual chlorine can reach the given value in about 10 min, with good response speed. From Figure 7 , it can be seen that the linear active disturbance rejection makes a relatively accurate estimation of the disturbance to the system and makes compensation to offset the impact of the disturbance on the system. Figure 8 , it can be seen that the effective chlorine dosage changes in response to the disturbance, keeping the residual chlorine at the given value.

[0088] The residual chlorine given value is set to 0.6 mg / L to test the system stability, and from Figure 9 , Figure 10 it can be seen that the residual chlorine can be stabilized within the given value of 0.6 ±0.1 mg / L, and the system has good stability.

[0089] The above embodiments are only preferred technical solutions of the present application, and should not be regarded as limiting the present application. The protection scope of the present application should be based on the technical solutions claimed in the claims, including equivalent replacement solutions of the technical features claimed in the claims. That is, within this range, equivalent replacement improvements are also within the protection scope of the present application.

Claims

1. A method for adding sodium hypochlorite based on linear active disturbance rejection control, characterized in that, Includes the following steps: S1. Data acquisition: Collect data on influent flow, chemical dosage, residual chlorine, and water temperature as the basic data source for subsequent control processes; S2. System modeling: The Seagull optimization algorithm is used to fit the water purification process system into a first-order transfer function model. S3, parameter calculation, calculate the bandwidth ω of setting the linear state error feedback control law according to the bandwidth method c and the extended state observer bandwidth ω o , calculate the system coefficient b0, the extended state observer feedback gain β1 and β2; S4. System Testing and Optimization: Test the system's control response speed and stability, and optimize control parameters based on actual production needs.

2. The sodium hypochlorite dosing method based on linear active disturbance rejection control according to claim 1, characterized in that, Step S2 is as follows: The water purification process system is simplified as a first-order transfer function as the controlled object, and a linear state error feedback control law and an extended state observer are designed. The extended state observer is designed as follows: ； In the formula, , , , ; Then, the Seagull algorithm is used to fit the controlled object into a first-order transfer function form.

3. The sodium hypochlorite dosing method based on linear active disturbance rejection control according to claim 2, characterized in that, The Seagull algorithm includes: (1) migration behavior To prevent each seagull from colliding with the surrounding seagulls, the position of the seagulls is adjusted using variable A; ； where: C S (t) is a new position that does not collide with other seagulls; P S (t) is the current position of the seagull; A is the motion behavior of the seagull; t is the current iteration number; the size of A is controlled by fc; ； Where: Kmax is the maximum number of iterations; fc is 2, and then all seagulls are moved toward the optimal seagull position; ； ； Where: M S (t) represents the optimal direction for the seagull; P S (t) represents the optimal location of the seagull; B is an important random parameter responsible for balancing the global and local searches of the algorithm, and rand is a random number in the range [0,1]; the seagull moves towards the optimal location of the seagull and reaches a new location D. S (t); ； (2) Aggressive behavior When attacking prey, seagulls move in a spiral pattern in the air; the attack position of the seagull is: ； Where: r is the helix radius; k is a random angle value, ranging from 0 to 2π; u and v are constants related to the helix shape; e is the base of the natural logarithm; Let the dosage from the collected n data points be the system input u, the residual chlorine in the treated water be the system output y, the fitting function output be f, and the fitting objective function be set as: ； ； Let s0 and T be variables in the transfer function. Use the Seagull algorithm to optimize and obtain the optimal fitting parameters.

4. The sodium hypochlorite dosing method based on linear active disturbance rejection control according to claim 3, characterized in that, In step S3, the expected response time is set to 300s, and the system controller bandwidth is... ,but 。 5. The sodium hypochlorite dosing method based on linear active disturbance rejection control according to claim 1, characterized in that, In step S4, during the system testing and optimization step, if the residual chlorine does not reach a given value within a specified time, or if the residual chlorine fluctuation exceeds a predetermined stable range, then ω is adjusted. c In addition to other relevant control parameters, the system was rerun and tested until the residual chlorine control accuracy and stability requirements were met, achieving the optimal operating state of the system.

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