Fuzzy adaptive PID (Proportion Integration Differentiation) flow control method, system, equipment and medium

Abstract

The invention relates to the technical field of automatic control, in particular to a fuzzy self-adaptive PID flow control method, system and device and a medium, and the method comprises the steps: collecting a mixed bed inlet flow actual value and an inlet flow set value, and calculating a deviation value and a deviation change value; inputting into a fuzzy reasoning module; a fuzzy rule base is constructed based on operation experience, a proportionality coefficient adjustment amount, an integral coefficient adjustment amount and a differential coefficient adjustment amount are dynamically generated, a control signal is further calculated, output is used for driving a pneumatic control valve, the opening degree of the valve is dynamically adjusted, and closed-loop control is formed; preferentially reducing a proportionality coefficient through a fuzzy rule, inhibiting instantaneous overshoot, adaptively adjusting an integral coefficient and a differential coefficient, and optimizing steady-state precision and anti-interference capability; through cooperation of the fuzzy self-adaptive PID algorithm and the high-precision pneumatic control valve, the industrial problems of large control overshoot, slow response and weak anti-interference under the large-flow working condition are solved, and high-precision, high-speed and high-robustness flow closed-loop control is achieved.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to a fuzzy adaptive PID flow control method, system, equipment and medium. Background Art

[0002] At present, the electric bypass door adopts an electric butterfly valve. From a structural point of view, the working principle of the butterfly valve is to adjust the flow of the medium by rotating the butterfly plate in the valve. When the butterfly plate rotates, the angle of the butterfly plate will change, thereby changing the area through which the fluid passes, so as to achieve the purpose of regulating the flow. However, when the butterfly valve stem rotates between 15° and 20°, the adjustment performance is not good, and it is easy to cause problems such as cavitation, erosion, vibration and noise; and when the butterfly valve stem rotates between 80° and 90°, the flow rate basically does not change. Therefore, the butterfly valve is not suitable for precise regulation of the mixed bed bypass flow, which has a great impact on the operation of the fine treatment mixed bed and the water quality of the effluent. The fine treatment bypass electric butterfly valve may not be opened in time due to failure or jamming, causing the mixed bed to trigger the automatic decoupling conditions and exit operation, which may cause abnormal shutdown of the unit in severe cases.

[0003] Secondly, the condensate flow control of the high-speed mixed bed directly affects the ion exchange efficiency and the water vapor quality of the unit. Traditional PID control algorithms are widely used in the control of flow control valves, but there are some problems in the adjustment process of large-size valves, such as insufficient stability: overshoot is prone to occur when the flow changes suddenly, resulting in system oscillation; slow response speed: traditional PID parameters are fixed and it is difficult to adapt to dynamic load changes; large steady-state error: flow deviations accumulate after long-term operation, affecting process accuracy. Although existing improvement methods can partially improve performance, they still rely on manual experience to adjust parameters, with delayed feedback and lack of real-time adaptive capabilities. Summary of the invention

[0004] In view of the fact that the above-mentioned existing PID control algorithm has insufficient stability, large steady-state error, still needs to rely on manual experience to adjust parameters, and lacks real-time adaptive capability, the present invention is proposed.

[0005] Therefore, the object of the present invention is to provide a fuzzy adaptive PID flow control method.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a fuzzy adaptive PID flow control method, comprising the following steps: Collect the actual value of the mixed bed inlet flow, the inlet flow set value, and calculate the deviation value and deviation change value in real time; Input the deviation value and the deviation change value into the fuzzy reasoning module; Based on the fuzzy rule base built on operating experience, the proportional coefficient adjustment, integral coefficient adjustment and differential coefficient adjustment are dynamically generated to further calculate the control signal, output the driving pneumatic control valve, dynamically adjust the valve opening and form a closed-loop control; The proportional coefficient is preferentially reduced through fuzzy rules to suppress instantaneous overshoot, and the integral coefficient and differential coefficient are adaptively adjusted according to the deviation change.

[0007] As a preferred solution of the fuzzy adaptive PID flow control method of the present invention, wherein: the fuzzy reasoning module includes the deviation value divided into 7 fuzzy subsets, including negative large, negative medium, negative small, zero, positive small, positive medium, and positive large; The fuzzy rule base includes at least one rule, when the deviation value is positive and large and the deviation change value is negative and large, the proportional coefficient is reduced and the differential coefficient is compensated weighted.

[0008] As a preferred solution of the fuzzy adaptive PID flow control method of the present invention, wherein: the adaptive adjustment includes a correction coefficient , , The decay function of is an exponential function, and the expression is:

[0009] in, is the proportional term correction coefficient, is the integral correction coefficient, is the correction coefficient of the differential term, is the exponential decay function value, is the initial value of the correction coefficient, is the decay rate constant, represents the exponential function, is the time elapsed from the initial moment.

[0010] As a preferred solution of a fuzzy adaptive PID flow control method of the present invention, the exponential decay function value not only decays with time, but is also associated with a deviation value or a deviation change value, so that the correction coefficient can adaptively adjust the decay rate according to the working conditions to optimize the control performance.

[0011] As a preferred solution of the fuzzy adaptive PID flow control method of the present invention, wherein: the fuzzy rule base constructed based on operating experience dynamically generates the proportional coefficient adjustment amount , integral coefficient adjustment , differential coefficient adjustment , update the PID parameter expression as: ; ; ; in, Represents the coefficient of the previous moment and the current adjustment The proportionality coefficient value is determined jointly by Represents the coefficient of the previous moment and the current adjustment The integral coefficient value is determined jointly by Represents the coefficient of the previous moment and the current adjustment The differential coefficient value determined jointly, , , Indicates at a point in time The controller sets the initial values ​​of different coefficients. , , is the parameter adjustment amount generated according to the fuzzy rule base.

[0012] As a preferred solution of a fuzzy adaptive PID flow control method of the present invention, the fuzzy rule base constructed based on operating experience generates a proportional coefficient value, an integral coefficient value and a differential coefficient value to calculate a control signal, and the control signal is expressed as: ; in, is the output value of the fuzzy adaptive PID controller, For at time point The controller output value is For at time point The deviation change of is the second-order backward difference of the error, which is used to approximate the second-order derivative of the error, For at time point The amount of deviation change.

[0013] As a preferred solution of the fuzzy adaptive PID flow control method of the present invention, in which: in the process of calculating the control signal, the time point of the historical deviation data The deviation change and the time point The deviation change is used to predict the traffic change trend, and the rule weight is optimized through the sliding window algorithm.

[0014] Another object of the present invention is to provide a fuzzy adaptive PID flow control system, which solves the problem that when the rotation amplitude of the butterfly valve stem is 80° to 90°, the flow rate does not change basically. Therefore, the butterfly valve is not suitable for precise adjustment of the mixed bed bypass flow rate, which has a great impact on the operation of the fine treatment mixed bed and the water quality of the effluent. The fine treatment bypass electric butterfly valve may not be opened in time due to failure or jamming, causing the mixed bed to trigger automatic decoupling conditions and exit operation. In severe cases, it causes abnormal shutdown of the unit. Through the coordination of the fuzzy adaptive PID algorithm and the high-precision pneumatic control valve, the industry problems of large control overshoot, slow response and weak anti-interference under large flow conditions are solved, and high-precision, high-speed and high-robustness flow closed-loop control is realized, providing an efficient and reliable solution for industrial process automation.

[0015] As a preferred solution of the fuzzy adaptive PID flow control system described in the present invention, it is characterized by comprising: Control valve, suitable for large flow change conditions; A positioner connected to one side of the regulating valve, used to convert an electrical signal into a pneumatic signal to drive the regulating valve to adjust its opening; A controller, used for calculating PID parameters according to the dynamic value of the deviation, and outputting a control signal to drive the positioner; The flow sensor is arranged in the upstream pipeline of the regulating valve, and is used to collect the actual value of the mixed bed inlet flow in real time and transmit it to the controller.

[0016] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of a method for improving short-time high-frequency energy storage efficiency are implemented.

[0017] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a method for improving the efficiency of short-time high-frequency energy storage are implemented.

[0018] Beneficial effects of the invention: The invention solves the industry problems of large control overshoot, slow response and weak anti-interference under large flow conditions through the cooperation of fuzzy adaptive PID algorithm and high-precision pneumatic control valve, realizes high-precision, high-speed and high-robustness flow closed-loop control, and provides an efficient and reliable solution for industrial process automation; the specific effects are as follows: Overshoot suppression and fast response: Prioritize reduction of proportional coefficient through fuzzy control rules , significantly reducing transient response overshoot, reducing flow overshoot control value, shortening time, effectively avoiding frequent valve oscillation, and improving system dynamic stability.

[0019] Improved steady-state accuracy: adaptively adjust the integral coefficient based on the deviation change value , when the deviation approaches zero, the integral effect is enhanced, the steady-state error is quickly eliminated, and the long-term flow fluctuation range is controlled within ±1%, meeting the industrial-grade high-precision requirements.

[0020] Optimizing noise immunity and robustness: Differentiation coefficients Dynamic compensation for load disturbances (such as sudden changes in pipeline pressure) combined with the disturbance adaptability design of the fuzzy rule base shortens the system recovery time and reduces the steady-state error under sudden flow changes, significantly improving the anti-interference ability under complex working conditions.

[0021] Precise control of large flow regulating valves: Aiming at the nonlinear characteristics of large-size valves of DN300 and PN40, high-precision mapping of valve opening and flow is achieved through fuzzy PID adaptive algorithm and equal percentage flow characteristic calibration.

[0022] Economic benefits and energy efficiency improvement: leakage is reduced, pressure drop loss is reduced, energy waste is significantly reduced, and the overall energy efficiency of the system is improved. It is suitable for high-energy-consuming industries such as electricity and chemical industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 An overall schematic diagram of a fuzzy adaptive PID flow control method provided for the first embodiment of the present invention.

[0025] Figure 2 A connection diagram of a fuzzy adaptive PID flow control system provided for the first embodiment of the present invention.

[0026] Figure 3 A schematic flow chart of a fuzzy adaptive PID flow control system provided for the first embodiment of the present invention.

[0027] Figure 4 This is a schematic diagram of the inlet flow rate curve of the high-speed mixed bed of Unit #3 when the present invention is applied.

[0028] Figure 5 This is a schematic diagram of the inlet flow rate curve of the high-speed mixed bed of Unit #4 when the present invention is applied. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0031] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0032] Secondly, the present invention is described in detail with reference to the schematic diagram. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0033] Example 1, reference Figure 1 to Figure 3 , which is the first embodiment of the present invention, provides a fuzzy adaptive PID flow control method, comprising the following steps: S1. Real-time collection of actual flow rate of mixed bed inlet , inlet flow setting value , generating deviation values and deviation change value ; in, For time point, For at time point Medium inlet flow setting value The actual value of the inlet flow Deviation value between the set value Issued by the DCS system, the range is 0-400m³ / h; S2. Value and The value is input into the fuzzy inference module, where: The values ​​are divided into 7 fuzzy subsets , where NB is negative and large, NM is negative and medium, NS is negative and small, ZE is zero, PS is positive and small, PM is positive and medium, and PB is positive and large; where the membership function of the fuzzy reasoning module is a triangular function, that is, the triangular function parameters: NB: Vertex (-4,1), Left Shoulder (-6,0), Right Shoulder (-2,0); ZE: Vertex (0,1), Left Shoulder (-2,0), Right Shoulder (2,0); PB: Vertex (4,1), Left Shoulder (2,0), Right Shoulder (6,0); (The remaining subsets are symmetrically distributed with equal spacing, and the center point interval is 1.33). Fuzzy rules are loaded according to Table 1, and control codes are generated through Matlab / Simulink; Table 1 Fuzzy rules , When the set flow rate is 400m³ / h, the control accuracy is 1%, the control deviation corresponds to the range of [-4, 4], and the corresponding maximum flow control deviation is ±1% (i.e. ±4m³ / h); S3, a fuzzy rule base built based on operational experience, the fuzzy rule base contains at least one rule, when The value is PB and When the value is NB, it is significantly reduced value, and The value is used to compensate the weight, the rule entries of the fuzzy rule base are dynamically linked with the temperature and pressure parameters of the condensate, the PID parameters are calibrated in real time, and the proportional coefficient adjustment amount is dynamically generated , integral coefficient adjustment , differential coefficient adjustment , the rule examples are as follows: Rule 1: If (very large deviation) and (deviation decreases rapidly), then: = −0.8 (significantly reducing the proportional term), =+0.5 (enhanced differential immunity); Rule 2: If (deviation close to zero) and (deviation increases slowly), then: =+0.3 (accelerates the convergence of integral); S4, the adaptive adjustment includes a correction coefficient , , The decay function of is an exponential function, and the expression is:

[0034] in, is the proportional term correction coefficient, is the integral correction coefficient, is the correction coefficient of the differential term, is the exponential decay function value, is the initial value of the correction coefficient, is the decay rate constant, represents the exponential function, is the time elapsed from the initial moment, the adaptive correction coefficient , , They are used to correct the speed, and their values ​​will decrease as the number of corrections increases; S5, the fuzzy rule base built based on operating experience dynamically generates the proportional coefficient adjustment amount , integral coefficient adjustment , differential coefficient adjustment , update the PID parameter expression as: ; ; ; in, Represents the coefficient of the previous moment and the current adjustment The proportionality coefficient value is determined jointly, Represents the coefficient of the previous moment and the current adjustment The integral coefficient value is determined jointly by Represents the coefficient of the previous moment and the current adjustment The differential coefficient value determined by both , , Indicates at a point in time The controller sets the initial values ​​of different coefficients. , , is the parameter adjustment amount generated according to the fuzzy rule base; Among them, real-time , , For dynamic adjustment, in the PID controller, The value is determined by the response speed of the system. The value can improve the response speed and reduce the steady-state deviation; however, Too large a value will result in a large overshoot, or even make the system unstable. The value can reduce overshoot and improve stability, but A value that is too small will slow down the response speed and prolong the adjustment time. Therefore, a larger value should be appropriately selected in the initial adjustment. value to improve the response speed, and in the mid-adjustment period, The value is taken as a smaller value to make the system have a smaller overshoot and ensure a certain response speed; and in the later stage of the adjustment process, Adjust the value to a larger value to reduce the static error and improve the control accuracy.

[0035] Among them, integral control The value is mainly used to eliminate the steady-state deviation of the system. Value changes in deviation When the value approaches zero, it increases to accelerate the elimination of steady-state errors. 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 the stability, its integral action should be relatively moderate; finally, in the later stage of the process, the integral action should be strengthened to reduce the static error of the regulation.

[0036] Among them, the differential coefficient of the system The value can reflect the trend of signal change and 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, eliminating oscillation, and ultimately changing the dynamic performance of the system. The selection of the value has a great influence on the dynamic characteristics of the regulation. If the value is too large, the brake will advance during the adjustment process, resulting in a long adjustment time; If the value is too small, the braking of the adjustment process will lag behind, resulting in an increase in overshoot. According to actual process experience, in the early stage of adjustment, the differential effect should be increased, so that a smaller or even no overshoot can be obtained; in the middle stage, due to the adjustment characteristics of K d The value is sensitive to changes, so The value should be appropriately smaller and should remain fixed; then in the later stage of adjustment, The value should be reduced to reduce the braking effect of the controlled process and compensate for the initial Larger values ​​will prolong the adjustment process.

[0037] S6. The fuzzy rule base constructed based on the operating experience generates a proportional coefficient value, an integral coefficient value and a differential coefficient value to calculate a control signal. The control signal is expressed as: ; in, is the output value of the fuzzy adaptive PID controller, For at time point The controller output value is For at time point The deviation change, is the second-order backward difference of the error, which is used to approximate the second-order derivative of the error, For at time point The deviation change of Wherein, the calculation control signal The time point of historical deviation data during the value calculation process The deviation change and at a point in time The deviation change Used to predict traffic change trends and optimize rule weights through a sliding window algorithm; Among them, the signal execution uses the historical deviation prediction mechanism as a sliding window algorithm for processing, and first needs to store the most recent historical deviation data , calculate the second-order difference of the deviation , predicting future traffic trends. Its application scenario is to increase the To suppress overshoot; S7, output drives the pneumatic regulating valve to dynamically adjust the valve opening to form a closed-loop control; S8. Use fuzzy rules to prioritize reducing the proportional coefficient, suppress instantaneous overshoot, and adaptively adjust the integral coefficient and differential coefficient according to the deviation change to improve steady-state accuracy and anti-disturbance capability; The dynamic performance optimization includes the overshoot suppression strategy as the proportional term priority: When the value is >3%, the fuzzy rule base triggers the logic of reducing the weight of the proportional item, for example: =−0.8⇒ =1.2+0.5*(−0.8)=0.8, by reducing , the valve opening rate slows down to avoid flow overshoot.

[0038] It also includes that the steady-state acceleration mechanism is an integral enhancement condition: when <0.1% / s (deviation changes slowly), triggering the integral term enhancement rule, for example: =+0.3⇒ =0.05+0.3*0.3=0.14. After the integral coefficient increases, the steady-state error elimination speed increases by 2 times.

[0039] Finally, anti-disturbance compensation is implemented as load disturbance detection: the pipeline pressure is monitored in real time through the pressure sensor. When a sudden pressure change (such as ±10% change) is detected, the differential compensation rule is triggered: =+0.5⇒ =0.3+0.4*0.5=0.5, the enhanced differential term suppresses the flow fluctuation caused by pressure fluctuation.

[0040] Embodiment 2 is the second embodiment of the present invention, which provides a fuzzy adaptive PID flow control system, specifically comprising: Control valve, valve body specifications DN300, PN40, the fluid channel in the valve body is S-shaped, the valve core adopts a double sealing surface structure, and the valve core is adapted to large flow change conditions; among them, the curvature radius of the S-shaped streamlined fluid channel is dynamically adapted to the valve opening, and the pressure drop loss is optimized through fluid mechanics simulation; A positioner connected to one side of the regulating valve, used to convert the electrical signal into a pneumatic signal to drive the regulating valve to adjust the opening; The controller is used to calculate the PID parameters according to the dynamic value of the deviation and output the control signal to drive the positioner; The flow sensor is installed in the upstream pipeline of the regulating valve to collect the actual value of the mixed bed inlet flow in real time and transmit it to the controller.

[0041] The controller is configured to receive Value and inlet flow setting value , and the generation bias Value and changes , dynamic adjustment of the rule base based on 7-level fuzzy subsets , , , and update the parameters through the formula to output the control signal Drives the pneumatic control valve.

[0042] Among them, a positioner is installed on one side of the regulating valve. The positioner is coupled to the valve stem through a gear or lever mechanism to ensure accurate transmission of the opening. The positioner has a built-in pressure fluctuation compensation algorithm, which shortens the response time and reduces the maximum stroke error. The pressure fluctuation compensation algorithm adjusts the output air pressure in real time according to changes in the air source pressure.

[0043] Embodiment 3 is the third embodiment of the present invention, which is different from the first two embodiments in that: If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0044] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0045] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0046] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0047] Example 4, reference Figure 4-5 , which is the third embodiment of the present invention, provides an intelligent pneumatic regulating valve system, specifically including: a high-speed mixed bed is set for the fine treatment of the #3 and #4 units of a power plant, and when the condensate amount is greater than the rated flow rate of the high-speed mixed bed, bypass treatment is required, and the flow rate is adjusted by the bypass regulating valve so that the flow rate entering the mixed bed is lower than the rated flow rate. However, the current bypass regulating valve is a butterfly valve with poor regulating performance. When the condensate amount entering the high-speed mixed bed is higher than the rated value, the high-speed mixed bed decoupling condition will be triggered, affecting the stable operation of the system.

[0048] The intelligent electrical regulating valve of the present invention is applied to the high-speed mixed bed bypass system of the power plant unit #3, and the fuzzy PID adaptive technology of the present invention is used to conduct industrial tests on the large flow regulating valve to regulate the flow. After the test, the high-speed mixed bed flow is set within the range of 150, 200, 250, 280, 300, and 350 m³ / h, and the actual water inlet flow of the mixed bed and the opening of the bypass regulating valve can remain stable. The specific values ​​and changes are shown in Table 2.

[0049] Table 2 High-speed mixed bed flow debugging , The operating curve of the high-speed mixed bed flow of unit #3 after stabilization is shown in the attached figure. Figure 4 As shown in the figure, when the condensate water quality is good, the mixed bed operation flow rate is set to 200m³ / h, and the actual operation flow rate fluctuates within the range of 200m³ / h±2m³ / h with a control accuracy of less than 1%. Even when the load increases or decreases rapidly during the unit's rapid peak regulation, the condensate volume fluctuates greatly, and the high-speed mixed bed operation flow curve fluctuates smoothly, with a good control effect.

[0050] Compared with Unit #4, the high-speed mixed bed bypass flow is still regulated by an electric butterfly valve. The mixed bed treatment flow change curve is shown in the attached figure. Figure 5 As shown, it can be seen that the mixed bed flow rate changes with the change of condensate amount and has poor stability. When it exceeds the maximum operating flow rate set for the high-speed mixed bed, the automatic protection program is triggered, the mixed bed is decoupled and exits operation, and the condensate bypass is fully opened, which affects the real-time processing of condensate and the quality of water vapor.

[0051] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A fuzzy adaptive PID flow control method, characterized in that: The following steps are included: Collect the actual value of the mixed bed inlet flow, the inlet flow set value, and calculate the deviation value and deviation change value in real time; Input the deviation value and the deviation change value into the fuzzy reasoning module; Based on the fuzzy rule base built on operating experience, the proportional coefficient adjustment, integral coefficient adjustment and differential coefficient adjustment are dynamically generated, and the control signal is calculated to output the driving pneumatic control valve to dynamically adjust the valve opening to form a closed-loop control; The proportional coefficient is preferentially reduced through fuzzy rules to suppress instantaneous overshoot, and the integral coefficient and differential coefficient are adaptively adjusted according to the deviation change.

2. A fuzzy adaptive PID flow control method according to claim 1, characterized in that: The fuzzy reasoning module includes the deviation value being divided into 7 fuzzy subsets, including negative large, negative medium, negative small, zero, positive small, positive medium, and positive large; The fuzzy rule base includes at least one rule, when the deviation value is positive and large and the deviation change value is negative and large, the proportional coefficient is reduced and the differential coefficient is compensated weighted.

3. A fuzzy adaptive PID flow control method according to claim 1 or 2, characterized in that: The adaptive adjustment includes a correction factor , , The decay function of is an exponential function, and the expression is: , in, is the proportional term correction coefficient, is the integral correction coefficient, is the correction coefficient of the differential term, is the exponential decay function value, is the initial value of the correction coefficient, is the decay rate constant, represents the exponential function, is the time elapsed from the initial moment.

4. A fuzzy adaptive PID flow control method according to claim 3, characterized in that: The exponential decay function value not only decays with time, but is also associated with a deviation value or a deviation change value, so that the correction coefficient can adaptively adjust the decay rate according to the working condition requirements to optimize the control performance.

5. A fuzzy adaptive PID flow control method according to claim 1, characterized in that: The fuzzy rule base built based on operating experience dynamically generates the proportional coefficient adjustment amount , integral coefficient adjustment , differential coefficient adjustment , update the PID parameter expression as: ； ； ； in, Represents the coefficient of the previous moment and the current adjustment The proportionality coefficient value is determined jointly, Represents the coefficient of the previous moment and the current adjustment The integral coefficient value is determined jointly by Represents the coefficient of the previous moment and the current adjustment The differential coefficient value determined by both , , Indicates at a point in time The controller sets the initial values ​​of different coefficients. , , is the parameter adjustment amount generated according to the fuzzy rule base.

6. A fuzzy adaptive PID flow control method according to claim 5, characterized in that: The fuzzy rule base constructed based on the operating experience generates a proportional coefficient value, an integral coefficient value and a differential coefficient value to calculate the control signal. The control signal is expressed as: ； in, is the output value of the fuzzy adaptive PID controller, For at time point The controller output value is For at time point The deviation change, is the second-order backward difference of the error, which is used to approximate the second-order derivative of the error, For at time point The amount of deviation change.

7. A fuzzy adaptive PID flow control method according to claim 6, characterized in that: In the process of calculating the control signal, the time point of the historical deviation data The deviation change and the time point The deviation change is used to predict the traffic change trend, and the rule weight is optimized through the sliding window algorithm.

8. A fuzzy adaptive PID flow control system, comprising a fuzzy adaptive PID flow control method as claimed in any one of claims 1 to 7, characterized in that: include: Control valve, suitable for large flow change conditions; A positioner connected to one side of the regulating valve, used to convert an electrical signal into a pneumatic signal to drive the regulating valve to adjust its opening; A controller, used for calculating PID parameters according to the dynamic value of the deviation, and outputting a control signal to drive the positioner; The flow sensor is arranged in the upstream pipeline of the regulating valve, and is used to collect the actual value of the mixed bed inlet flow in real time and transmit it to the controller.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a fuzzy adaptive PID flow control method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a fuzzy adaptive PID flow control method according to any one of claims 1 to 7 are implemented.

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