Sewage pipeline hydrogen sulfide oxygenation control method based on fuzzy PID

Abstract

The invention relates to the technical field of municipal sewage pipeline hydrogen sulfide control, in particular to a sewage pipeline hydrogen sulfide oxygenation control method based on fuzzy PID. The hydrogen sulfide control method comprises the following steps: acquiring the concentration of hydrogen sulfide in a sewage pipeline in real time through a sensor; comparing the actual concentration of the hydrogen sulfide with the set concentration to obtain the deviation e (t) and the deviation variation ec (t) of the hydrogen sulfide concentration; the deviation e (t) and the deviation variation ec (t) serve as input parameters of a fuzzy controller, and the rotating speed of an oxygenation fan is adjusted according to the output value of the fuzzy PID; the concentration of hydrogen sulfide in the pipeline is kept within a set safety range by oxygenating the sewage pipeline. According to the invention, precise frequency modulation control is carried out on the aerator through fuzzy PID, refined and efficient control of H2S in the municipal sewage pipeline is realized, and the problems of odor disturbance, health risk, pipeline corrosion and the like are further reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of hydrogen sulfide control in municipal sewage pipes, and in particular relates to a sewage pipe hydrogen sulfide oxygenation control method based on fuzzy PID. Background Art

[0002] With the rapid development of urbanization and industrialization, people's demand for a comfortable living environment is increasing day by day. As an important part of municipal construction, the urban sewage pipe system plays an irreplaceable and important role in the collection and transportation of various types of sewage and wastewater, as well as the final treatment and discharge. However, since the sewage pipe network system has been in a relatively closed state for a long time, an anaerobic environment has been formed inside it, and microorganisms will produce easily diffusible and highly toxic hydrogen sulfide (H2S) gas through growth and metabolic activities in this environment, which will not only produce odor, but also corrode the sewage pipes, greatly affecting the safe operation of the drainage pipes and the daily life of the surrounding people. Therefore, it is necessary to construct a sewage pipe hydrogen sulfide oxygenation control system to control the generation of hydrogen sulfide, thereby reducing the occurrence of sewage pipe safety accidents.

[0003] In the current field of engineering technology, the control system based on parameters such as proportion, integration, and differentiation is a widely used control theory, also known as PID regulation. After more than seventy years of development, PID control technology has become increasingly mature and perfect, with advantages such as simple structure, stable operation, and easy adjustment. In the current field of industrial control technology, this control method accounts for about 85%-90%. Especially for situations where the control object has a complex structure, it is difficult to obtain parameters, and other control technology theories cannot meet application requirements, the application value of PID regulation is particularly significant. However, the hydrogen sulfide concentration control system is a dynamic system with variable parameters, time lags, and random interferences, and traditional PID cannot adjust PID parameters in real time. Therefore, the prior art requires a method that can control PID parameters in real time to improve the accuracy and stability of regulating the oxygen enrichment fan. Summary of the invention

[0004] The purpose of this patent is to provide a sewage pipe hydrogen sulfide oxygenation control method based on fuzzy PID, which can accurately control the oxygenation fan through fuzzy PID, thereby realizing refined and efficient control of H2S in municipal sewage pipes, reducing odor nuisance, health risks and pipeline corrosion. This solves the problem that the traditional PID mentioned in the background technology cannot adjust the PID parameters in real time.

[0005] A sewage pipe hydrogen sulfide oxygenation control method based on fuzzy PID, characterized in that it comprises the following steps:

[0006] Step 1: System detection and initialization of system parameters;

[0007] Step 2: Obtain the current hydrogen sulfide concentration in the sewage pipe;

[0008] Step 3: Compare the actual concentration of hydrogen sulfide with the set concentration, and obtain the deviation e(t) and deviation change ec(t) of hydrogen sulfide concentration as input parameters of the fuzzy controller;

[0009] Step 4: Construct a fuzzy controller to calculate K p , K i , K d The three parameters are adjusted online and output to the PID controller;

[0010] Step 5: Evaluate the fuzzy PID control model, and stop optimizing its parameters when the preset requirements are met;

[0011] Step 6: The oxygen enrichment fan is controlled by a PID controller to adjust the concentration of hydrogen sulfide in the sewage pipe.

[0012] Furthermore, the system detection and initialization of system parameters described in step 1 include: detecting sensors and fan status, and initializing PID controller parameters.

[0013] Furthermore, the method for obtaining the current hydrogen sulfide concentration in the sewage pipe in step 2 is to detect the concentration of hydrogen sulfide in the gas phase of the sewage pipe by using a hydrogen sulfide sensor.

[0014] Furthermore, the calculation formulas for the deviation e(k) and the deviation change ec(k) of the hydrogen sulfide concentration in step 3 are respectively:

[0015] e(t)=r(t)-c(t)

[0016] ec(t)=e(t)-e(t-1)

[0017] Among them, r(t) and c(t) represent the set concentration and actual concentration of hydrogen sulfide in the sewage pipe during the control process at time t; e(t) and e(t-1) represent the control deviation of hydrogen sulfide concentration in the sewage pipe at time t and time t-1 respectively; ec(t) represents the change of hydrogen sulfide concentration deviation at time t;

[0018] Furthermore, in step 4, a fuzzy controller is constructed to p , K i , K d Three parameters are adjusted online and output to the PID controller, including:

[0019] Step 401: Blurring

[0020] ① Determination of fuzzy domain

[0021] According to the set value and actual value of hydrogen sulfide concentration in the sewage pipe, the value range of e(t) and ec(t) is determined, thereby determining the domain of e(t) and ec(t).

[0022] ②Fuzzy classification

[0023] The domain is divided into 6 ranges, and the 7 endpoints are graded into fuzzy subsets: NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium), and PB (positive large).

[0024] ③Determination of membership function

[0025] Common membership functions include triangular membership function, trapezoidal membership function, parabolic membership function, and the value range of the membership function is [0,1]. The present invention selects a triangular function as the membership function, which has a simple structure and can meet the requirements of control accuracy. In order to simplify the fuzzy PID algorithm, this paper uses e, ec, ΔK p , ΔK i , ΔK d The membership functions of the five quantities are all triangular functions, and their mathematical expressions are:

[0026]

[0027] ④ Determination of membership

[0028] Map the value of e(t) corresponding to the domain onto the membership function, and the corresponding two values ​​are the membership of e(t) to the two fuzzy subsets respectively; similarly, calculate the membership of ec(t) to the two fuzzy subsets.

[0029] Step 402: Fuzzy Reasoning

[0030] ① Establish ΔK p , ΔK i , ΔK d Fuzzy rule table

[0031] ② Combine the membership of e(t) and ec(t) in pairs, and check the rule table in ① to get the membership of e(t) and ec(t) and ΔK p , ΔK i , ΔK d relationship.

[0032] ③Calculate ΔK p , ΔK i , ΔK d The degree of membership of each fuzzy subset

[0033] Step 403: Deblurring

[0034] ① Calculate ΔK by multiplying the membership degree by the corresponding membership value p , ΔK i , ΔK d

[0035] ②Calculate K p , K i , K d

[0036]

[0037] Among them, K p , K i , K d They are proportional coefficient, integral coefficient and differential coefficient respectively, which are the parameter values ​​after PID correction; K p0 , K i0 , K d0 They are the initial values ​​of PID parameter tuning; ΔK p , ΔK i , ΔK d are the change values ​​of their corresponding coefficients respectively.

[0038] ③Calculate the fuzzy PID output value

[0039]

[0040] Among them, u(t) is the control quantity output by the PID controller; e(t) is the deviation value of the hydrogen sulfide concentration; and t is the tth moment.

[0041] Furthermore, in step 5, the fuzzy PID control model is evaluated, and when the preset requirements are met, the optimization of its parameters is stopped. The preset requirements include: the fuzzy PID control model reaches a stable operating state, the response time of the stable operating state is as short as possible, and the overshoot of the stable operating state is as small as possible.

[0042] Furthermore, in step 6, the oxygenating fan is controlled by a PID controller to adjust the concentration of hydrogen sulfide in the sewage pipe, including: inputting the setting result of step 4 into the PID controller, and the PID controller calculates and outputs the speed signal of the oxygenating fan at this time through proportional, integral and differential operations, thereby controlling the speed of the oxygenating fan to control the concentration of hydrogen sulfide in the sewage pipe within a set range.

[0043] Beneficial effects of the present invention:

[0044] The invention proposes a sewage pipe hydrogen sulfide oxygenation control method based on fuzzy PID, which inputs the hydrogen sulfide concentration deviation e(t) and the deviation change ec(k) into the fuzzy controller, and p , K i , Kd The three parameters are adjusted online and output to the PID controller. The fuzzy PID is used to precisely control the frequency of the oxygen enrichment fan, thereby achieving refined and efficient control of H2S in the municipal sewage pipe, reducing odor nuisance, health risks, and pipe corrosion. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of the hydrogen sulfide oxygenation control method of the present invention;

[0046] Figure 2 This is a block diagram of the hydrogen sulfide fuzzy PID oxygenation control of the present invention;

[0047] Figure 3 The triangle membership function diagram used in the present invention DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] like Figure 1 As shown, a sewage pipe hydrogen sulfide oxygenation control method based on fuzzy PID includes the following steps:

[0050] Step 1: System detection and initialization of system parameters; including detection of sensors and fan status, and initialization of PID controller parameters.

[0051] Step 2: Detect the concentration of hydrogen sulfide in the gas phase of the sewage pipe through a hydrogen sulfide sensor.

[0052] Step 3: Compare the actual concentration of hydrogen sulfide with the set concentration to obtain the deviation e(k) and deviation change ec(k) of hydrogen sulfide concentration. The calculation formulas are:

[0053] e(t)=r(t)-c(t)

[0054] ec(t)=e(t)-e(t-1)

[0055] Among them, r(t) and c(t) represent the set concentration and actual concentration of hydrogen sulfide in the sewage pipe during the control process at time t; e(t) and e(t-1) represent the control deviation of hydrogen sulfide concentration in the sewage pipe at time t and time t-1 respectively; ec(t) represents the change of hydrogen sulfide concentration deviation at time t;

[0056] Step 4: Figure 2As shown, construct a fuzzy controller to p , K i , K d The three parameters are adjusted online and output to the PID controller, including the following steps:

[0057] Step 401: Blurring

[0058] ① Determination of fuzzy domain

[0059] According to the measurement range of hydrogen sulfide concentration in the sewage pipe, the maximum and minimum values ​​are recorded as max and min respectively, so as to determine the range of deviation e(t) as [min-max, max-min], and the range of deviation change ec(t) is twice as large as [2(min-max), 2(max-min)]. The domain of e(t) and ec(t) is defined as {-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6}, and the linear quantization is adopted, and the functional relationship is:

[0060]

[0061]

[0062] ②Fuzzy classification

[0063] The domain is divided into 6 ranges, and the 7 endpoints are graded into fuzzy subsets: -6→NB (negative large), -4→NM (negative medium), -2→NS (negative small), 0→ZO (zero), 2→PS (positive small), 4→PM (positive medium), and 6→PB (positive large).

[0064] ③Determination of membership function

[0065] Common membership functions include triangular membership function, trapezoidal membership function, parabolic membership function, and the value range of the membership function is [0,1]. The present invention selects the triangular function as the membership function. Figure 3 As shown, its structure is simple and can meet the requirements of control accuracy. In order to simplify the fuzzy PID algorithm, this paper uses e, ec, ΔK p , ΔK i , ΔK d The membership functions of the five quantities are all triangular functions, and their mathematical expressions are:

[0066]

[0067] The value range of parameters a and b is [-6, 6].

[0068] ④ Determination of membership

[0069] Map the value of e(t) corresponding to the domain onto the membership function, and the corresponding two values ​​are the membership of e(t) to the two fuzzy subsets respectively; similarly, find the membership of ec(t) to the two fuzzy subsets. For example, if the quantized result is -5.5, between -6 and -4, if the triangular membership function is used, the membership corresponding to -6 (NB) is 0.75, and the membership corresponding to -4 (NM) is 0.25, that is, the membership to NB is a, and the membership to NM is 1-a, and the fuzzification is now complete.

[0070] Step 402: Fuzzy Reasoning

[0071] ① Establish ΔK p , ΔK i , ΔK d Fuzzy rule table

[0072] In the PID controller, the selection of Kp value is determined by the response speed of the system. Increasing Kp can improve the response speed and reduce the steady-state deviation; however, if the Kp value is too large, it will produce a large overshoot and even make the system unstable. Reducing Kp can reduce overshoot and improve stability, but if Kp is too small, it will slow down the response speed and extend the adjustment time. Therefore, in the early stage of adjustment, a larger Kp value should be appropriately taken to improve the response speed, while in the middle stage of adjustment, Kp should be taken to a smaller value so that the system has a smaller overshoot and ensures a certain response speed; and in the later stage of the adjustment process, the Kp value is adjusted to a larger value to reduce the static error and improve the control accuracy.

[0073] In system control, integral control is mainly used to eliminate the steady-state deviation of the system. Due to some reasons (such as saturation nonlinearity, etc.), the integral process may produce integral saturation in the early stage of the regulation process, thereby causing a large overshoot of the regulation process. Therefore, in the early stage of the regulation process, in order to prevent integral saturation, its integral action should be weaker, or even zero; in the middle stage of the regulation, in order to avoid affecting stability, its integral action should be moderate; finally, in the late stage of the process, the integral action should be strengthened to reduce the static error of the regulation.

[0074] The adjustment of the differential link is mainly introduced for the large inertia process, and the role of the differential link coefficient is to change the dynamic characteristics of the system. The differential link coefficient of the system can reflect the trend of signal change, and can introduce an effective early correction signal into the system before the deviation signal changes too much, thereby speeding up the response speed, reducing the adjustment time, and eliminating oscillation. Ultimately, it changes the dynamic performance of the system. Therefore, the selection of the Kd value has a great influence on the dynamic characteristics of the regulation. If the Kd value is too large, the braking of the regulation process will be ahead, resulting in a long adjustment time; if the Kd value is too small, the braking of the regulation process will lag behind, resulting in an increase in overshoot. According to actual process experience, in the early stage of regulation, the differential effect should be increased, so that a smaller or even no overshoot can be obtained; in the middle stage, since the regulation characteristics are more sensitive to the change of the Kd value, the Kd value should be appropriately smaller and should remain fixed; then in the later stage of regulation, the Kd value should be reduced to reduce the braking effect of the controlled process, thereby compensating for the extension of the regulation process time caused by the large Kd value in the early stage of the regulation process.

[0075] ② Combine the membership of e(t) and ec(t) in pairs, and check the rule table in ① to get the membership of e(t) and ec(t) and ΔK p , ΔK i , ΔK d relationship.

[0076] Assume that the two membership values ​​of e(t) are PM and PB, and the membership of e(t) to PM is a (a<1), then the membership to PB is (1-a). Assume that the two membership values ​​of ec(t) are NB and NM, and the membership of ec(t) to NM is b, then the membership to NB is (1-b).

[0077] ③Calculate ΔK p , ΔK i , ΔK d The degree of membership of each fuzzy subset

[0078] Assume that the membership of e(k) to PM is a, and the membership of ec(t) to NB is (1-b), then the membership of the output value to ZO is a*(1-b). Similarly, we can conclude that when the other two memberships of the output value to ZO are a*b and (1-a)*(1-b), and the membership of the output value to NS is (1-a)*(1-b), the sum of the four memberships is equal to 1.

[0079] Step 403: Deblurring

[0080] ① Calculate ΔK by multiplying the membership degree by the corresponding membership value p , ΔK i , ΔK d

[0081] For the output value, we also use the method of giving membership, based on the previously designed triangular membership function, and use the same domain [-6, 6]. Therefore, at a certain moment, the membership of the fuzzy variables where the input and output are located is the same. Based on this basis, we use the centroid method to calculate the quantitative value of each output quantity. The formula is as follows:

[0082]

[0083] Among them, y is the precise value of the fuzzy controller output after defuzzification, that is, ΔK p , ΔK i , ΔK d ;z i is the value in the domain of fuzzy control quantity; μ Ai (x), μ Bi (y) is z i The membership degree of . It is the sum of the memberships of the output values, and the sum of the memberships calculated in any direction is 1, so the denominator can be omitted.

[0084] For example, we assume that the output value is [0, 120] (i.e., the range of hydrogen sulfide concentration) and is also divided into 6 parts, i.e., 7 membership values ​​NB, NM, NS, ZO, PS, PM, and PB. Based on the conclusion drawn in the previous step, we can calculate the solution of the output value by multiplying the membership degree by the corresponding membership value, i.e.

[0085] y=[a×b+a(1-b)+(1-a)(1-b)]×ZO+(1-a)×b×NS

[0086] ②Calculate K p , K i , K d

[0087]

[0088] Among them, K p , K i , K d They are proportional coefficient, integral coefficient and differential coefficient respectively, which are the parameter values ​​after PID correction; K p0 , K i0 , K d0 They are the initial values ​​of PID parameter tuning; ΔK p , ΔK i , ΔK d are the change values ​​of their corresponding coefficients respectively.

[0089] ③Calculate the fuzzy PID output value

[0090]

[0091] Among them, u(t) is the control quantity output by the PID controller; e(t) is the deviation value of the hydrogen sulfide concentration; and t is the tth moment.

[0092] Step 5: Evaluate the fuzzy PID control model. When the preset requirements are met, stop optimizing its parameters. The preset requirements include: the fuzzy PID control model reaches a stable operating state, the response time of the stable operating state is as short as possible, and the overshoot of the stable operating state is as small as possible.

Claims

1. A sewage pipe hydrogen sulfide oxygenation control method based on fuzzy PID, characterized in that: The following steps are involved: Step 1: System detection and initialization of system parameters; Step 2: Obtain the current hydrogen sulfide concentration in the sewage pipe; Step 3: Compare the actual concentration of hydrogen sulfide with the set concentration, and obtain the deviation e(t) and deviation change ec(t) of hydrogen sulfide concentration as input parameters of the fuzzy controller; Step 4: Construct a fuzzy controller to calculate K p , K i , K d The three parameters are adjusted online and output to the PID controller; Step 5: Evaluate the fuzzy PID control model, and stop optimizing its parameters when the preset requirements are met; Step 6: The oxygen enrichment fan is controlled by a PID controller to adjust the concentration of hydrogen sulfide in the sewage pipe.

2. A sewage pipe hydrogen sulfide oxygenation control method based on fuzzy PID according to claim 1, characterized in that: The system detection and initialization of system parameters described in step 1 include: detecting the sensor and fan status, and initializing the PID controller parameters.

3. A sewage pipe hydrogen sulfide oxygenation control method based on fuzzy PID according to claim 1, characterized in that: The method for obtaining the current hydrogen sulfide concentration in the sewage pipe in step 2 is to detect the concentration of hydrogen sulfide in the gas phase of the sewage pipe through a hydrogen sulfide sensor.

4. A sewage pipe hydrogen sulfide oxygenation control method based on fuzzy PID according to claim 1, characterized in that: The calculation formulas for the deviation e(t) and the deviation change ec(t) of the hydrogen sulfide concentration in step 3 are: e(t)=r(t)-c(t) ec(t)=e(t)-e(t-1) Among them, r(t) and c(t) represent the set concentration and actual concentration of hydrogen sulfide in the sewage pipe during the control process at time t; e(t) and e(t-1) represent the control deviation of hydrogen sulfide concentration in the sewage pipe at time t and time t-1 respectively; ec(t) represents the change of hydrogen sulfide concentration deviation at time t.

5. A sewage pipe hydrogen sulfide oxygenation control method based on fuzzy PID according to claim 1, characterized in that: In step 4, a fuzzy controller is constructed to p , K i , K d Three parameters are adjusted online and output to the PID controller, including: Step 401: Blurring ① Determination of fuzzy domain According to the set value and actual value of hydrogen sulfide concentration in the sewage pipe, the value range of e(t) and ec(t) is determined, thereby determining the domain of e(t) and ec(t). ②Fuzzy classification The domain is divided into 6 ranges, and the 7 endpoints are graded into fuzzy subsets: NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium), and PB (positive large). ③Determination of membership function Common membership functions include triangular membership function, trapezoidal membership function, parabolic membership function, and the value range of the membership function is [0,1]. The present invention selects a triangular function as the membership function, which has a simple structure and can meet the requirements of control accuracy. In order to simplify the fuzzy PID algorithm, this paper uses e, ec, ΔK p , ΔK i , ΔK d The membership functions of the five quantities are all triangular functions, and their mathematical expressions are: ④ Determination of membership Map the value of e(t) corresponding to the domain onto the membership function, and the corresponding two values ​​are the membership of e(t) to the two fuzzy subsets respectively; similarly, calculate the membership of ec(t) to the two fuzzy subsets. Step 402: Fuzzy Reasoning ① Establish ΔK p , ΔK i , ΔK d Fuzzy rule table ② Combine the membership of e(t) and ec(t) in pairs, and check the rule table in ① to get the membership of e(t) and ec(t) and ΔK p , ΔK i , ΔK d relationship. ③Calculate ΔK p , ΔK i , ΔK d The degree of membership of each fuzzy subset Step 403: Deblurring ① Calculate ΔK by multiplying the membership degree by the corresponding membership value p , ΔK i , ΔK d ②Calculate K p , K i , K d Among them, K p , K i , K d They are proportional coefficient, integral coefficient and differential coefficient respectively, which are the parameter values ​​after PID correction; K p0 , K i0 , K d0 They are the initial values ​​of PID parameter tuning; ΔK p , Δk I , ΔK D are the change values ​​of their corresponding coefficients respectively. ③Calculate the fuzzy PID output value Among them, u(t) is the control quantity output by the PID controller; e(t) is the deviation value of the hydrogen sulfide concentration; and t is the tth moment.

6. A sewage pipe hydrogen sulfide oxygenation control method based on fuzzy PID according to claim 1, characterized in that: In step 5, the fuzzy PID control model is evaluated, and when the preset requirements are met, the optimization of its parameters is stopped. The preset requirements include: the fuzzy PID control model reaches a stable operating state, the response time of the stable operating state is as short as possible, and the overshoot of the stable operating state is as small as possible.

7. A sewage pipe hydrogen sulfide oxygenation control method based on fuzzy PID according to claim 1, characterized in that: In step 6, the oxygenating fan is controlled by a PID controller to adjust the concentration of hydrogen sulfide in the sewage pipe, including: inputting the setting result of step 4 into the PID controller, and the PID controller calculates and outputs the speed signal of the oxygenating fan at this time through proportional, integral and differential operations, thereby controlling the speed of the oxygenating fan to control the concentration of hydrogen sulfide in the sewage pipe within a set range.

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