Variable universe fuzzy fractional order PID (Proportion Integration Differentiation) control method for NOx concentration in flue gas denitration

Abstract

The invention discloses a variable universe fuzzy fractional order PID (Proportion Integration Differentiation) control method for NOx concentration in flue gas denitrification. The method comprises the following steps: firstly, determining initial parameters of a fractional order PID controller; secondly, a fuzzy controller is designed, a fuzzy rule and input and output variables of the fuzzy controller are determined, the input variables are NOx mass concentration errors and error change rates, and the output variables are variable quantities of parameters of the fractional order PID controller; then, a variable universe fuzzy system is designed, the input variables are the NOx mass concentration error and the error change rate, and three outputs serve as the input error in a fuzzy controller, the variable universe scaling factor of the error change rate and the variable universe scaling factor of the output fractional order PID controller parameter variable quantity respectively; and finally, combining the initial parameters of the fractional order PID controller with the parameter variation output by the fuzzy controller to serve as final parameters of the fractional order PID controller. The dynamic and steady-state performance of the NOx mass concentration is achieved, and the environmental protection index requirement is better met.

Description

Technical Field

[0001] The present invention relates to a variable universe fuzzy fractional order PID control method for the NOx concentration in flue gas denitrification, belonging to the technical field of automatic control of the NOx concentration in flue gas denitrification. Background Technique

[0002] In thermal power units, the selective catalytic reduction (SCR) technology is often used for flue gas denitrification treatment. Due to its advantages such as simple structure, small floor area, and little interference to the boiler, it has been widely applied. However, due to the large delay and inertia in the SCR system, traditional PID controllers often cannot achieve ideal control effects when adjusting the flue gas NOx concentration, and the control accuracy and response speed need to be improved.

[0003] Regarding this problem, researchers have proposed various improved control methods, such as the general fractional order PID controller, etc. Although these methods have improved the control performance of the system to a certain extent, due to the complex dynamic characteristics of the SCR system, it is still difficult to achieve the balance between fast response and high stability. Therefore, how to design more accurate and efficient control strategies has become a research hotspot in the current field of flue gas denitrification control. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a variable universe fuzzy fractional order PID control method for the NOx concentration in flue gas denitrification, which improves the control accuracy of the NOx mass concentration and better meets the requirements of environmental protection indicators.

[0005] The present invention adopts the following technical solutions to solve the above technical problems: A variable universe fuzzy fractional order PID control method for the NOx concentration in flue gas denitrification includes the following steps: Step 1, design a fractional order PID controller for the flue gas denitrification system to control the NOx concentration output by the flue gas denitrification system, and use an improved population search algorithm to determine the initial values of the parameters of the fractional order PID controller; Step 2, obtain the NOx mass concentration error and the error change rate of the flue gas denitrification system, and use them as the input variables of the fuzzy controller. Take the change amount of the parameters of the fractional order PID controller as the output variable of the fuzzy controller, and determine the fuzzy rules of the output variable of the fuzzy controller; Step 3, select a construction method of the scaling factor based on the fuzzy inference form, design a variable universe fuzzy system, and use and as the inputs of the variable universe fuzzy system, and use the and output by the variable universe fuzzy system as the scaling factors of the input variables of the fuzzy controller, and use the As the scaling factor of the output variable of the fuzzy controller, the variable universe processing is performed on the input and output universes of discourse of the fuzzy controller; Step 4: Combine the change amount of the fractional-order PID controller parameters and the initial values of the fractional-order PID controller parameters output by the fuzzy controller after the variable universe processing to obtain the fractional-order PID controller parameters, and realize the adaptive control of the NOx concentration output by the flue gas denitration system.

[0006] Compared with the prior art, the present invention adopts the above technical solutions and has the following technical effects: 1. The method proposed by the present invention realizes the dynamic and steady-state performance of the NOx mass concentration, improves the control accuracy of the NOx mass concentration, and better meets the requirements of environmental protection indicators.

[0007] 2. The method proposed by the present invention has better control effect than the existing general fractional-order PID. Compared with the fuzzy fractional-order PID controller, it has obvious advantages in the peak time index, overshoot index, and steady-state error index without significantly sacrificing the rise time and adjustment time. Brief Description of the Drawings

[0008] Figure 1 is the schematic diagram of the variable universe fuzzy fractional-order PID control of the NOx concentration in the flue gas denitration of the present invention; Figure 2 is the variable universe fuzzy fractional-order PID control model diagram based on Simulink; Figure 3 is the simulation comparison diagram of the control effect based on Simulink. Specific Embodiments

[0009] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention and should not be construed as limiting the present invention.

[0010] As Figure 1 shown, the present invention proposes a variable universe fuzzy fractional-order PID control method for the NOx concentration in flue gas denitration, and the specific steps are as follows: Step 1: Design a general fractional-order PID controller and determine the initial parameters of the fractional-order PID controller; The fractional-order PID control system is expressed as: , In the formula, is the PID controller combined with fractional calculus, is the proportional coefficient, is the integral coefficient, is the differential coefficient, is the integration order, is the differential order, is the complex frequency domain variable; in particular, when and , is a traditional PID controller.

[0011] Determine the initial values of the fractional-order PID controller parameters, including: the initial value of the proportional coefficient , the initial value of the integral coefficient , the initial value of the differential coefficient , the integration order , the differential order .

[0012] Step 2: Design a fuzzy controller, determine its fuzzy rules and input / output variables, and determine the quantization factors of the input / output variables; The NOx mass concentration error and the error change rate are used as inputs, and the change amounts of the fractional-order PID proportional coefficient , the change amount of the integral coefficient , and the change amount of the differential coefficient are used as outputs. Fuzzify the input / output variables. The fuzzy sets of the input variables and output variables are both , which represent negative large, negative medium, negative small, zero, positive small, positive medium, and positive large respectively. The membership functions are all selected as triangular functions; Determine reasonable fuzzy rules as shown in Table 1 - Table 3, and determine the quantization factors of the NOx mass concentration error , the error change rate , the change amount of the proportional coefficient , the change amount of the integral coefficient , and the change amount of the differential coefficient .

[0013] Table 1 Fuzzy rules of

[0014] Table 2 Fuzzy rules of

[0015] Table 3 Fuzzy rules of

[0016] Step 3: Select a construction method of the scaling factor based on the fuzzy inference form, design a variable universe fuzzy system, determine its input / output variables, and determine the quantization factors of the input / output variables; The NOx mass concentration error Sum of error change rate As the input, the output 、 And Are respectively used as the scaling factors of the error and the error change rate in the variable universe of discourse as the input, and the scaling factor of the change amount of the controller parameter of the output, and the input and output variables are fuzzified; And The fuzzy sets of are Respectively representing negative large, negative medium, negative small, zero, positive small, positive medium, positive large; And The fuzzy sets of are Respectively representing slight compression, medium compression, basically unchanged, and slight expansion, The fuzzy sets of are Respectively representing large compression, medium compression, slight compression, remaining unchanged, slight expansion, medium expansion, and large expansion; the membership functions are all selected as triangular functions, the fuzzy rules are determined as shown in Table 4 - Table 6, and the quantization factors of the input variable NOx mass concentration error 、Error change rate Are determined.

[0017] Table 4 The fuzzy rules of

[0018] Table 5 The fuzzy rules of

[0019] Table 6 The fuzzy rules of

[0020] The input and output universes of discourse of the fuzzy controller become after being processed by the variable universe of discourse rules: , In the formula, Is the initial universe of discourse of the input variable error Of the fuzzy controller, Is the initial universe of discourse of the input variable error change rate Of the fuzzy controller, Is the output variable of the fuzzy controller 、 、 Of the initial universe of discourse; 、 、 Are the universes of discourse after the variable universe of discourse adjustment.

[0021] Step 4: Combine the initial parameters of the controller with the parameter variation output by the fuzzy controller as the final fractional-order PID controller parameters; The online self-tuning formula for the fractional-order PID controller parameters is expressed as: , where, , , are the initial parameters of the fractional-order PID controller, , , are the controller parameter variations after defuzzification, , , are the control parameters of the corrected fractional-order PID. The integral order , and the differential order are the same as the initial parameters of the fractional-order PID.

[0022] Embodiment In the SCR denitration system, during the operation of the cascade control system, the main regulator is used to adjust the NO x mass concentration in the flue gas. The mass concentration of NO x in the flue gas has the transfer function of: , The improved swarm optimization algorithm (SOA algorithm) is based on the SOA algorithm. Aiming at the problem that the searchers are prone to fall into local optimum, an optimization strategy is proposed to dynamically sort the search population according to the fitness value and introduce random mutation operations for individuals with poor fitness. The fractional-order PID controller parameters optimized by the improved SOA algorithm are used as the Figure 1 initial values of the fractional-order PID controller in , , , , .

[0023] Design a fuzzy controller with the error between the NOx concentration and the set NOx concentration and the error change rate as the inputs, and the variation of the fractional-order PID proportional coefficient , the variation of the integral coefficient and the variation of the differential coefficient as the outputs. Assume that the grade quantity domains of the input error and the error change rate of the fuzzy controller are both , and the grade quantity domains of the outputs are both ; among the input variables, the error The quantization factor of and the error change rate is ; among the output variables the quantization factor of and the quantization factor of and the quantization factor of .

[0024] Select a scaling factor construction method based on the fuzzy inference form to design a variable universe fuzzy system. The NOx mass concentration error and the error change rate are used as inputs, and the outputs , and are respectively used as the scaling factors of the error, the error change rate in the input of the variable universe, and the change amount of the controller parameter in the output. The graded quantity universes of the input and output quantities are both . Among the input variables, the quantization factor of the error is , and the quantization factor of the error change rate is ; there is no quantization factor for the output variable.

[0025] Figure 2 is the diagram of the variable universe fuzzy fractional - order PID control model. The simulation results based on Simulink are as Figure 3 shown. The variable universe fuzzy fractional - order PID controller is significantly superior to the existing general fractional - order PID controller. Compared with the fuzzy fractional - order PID controller, from the performance indicators in Table 7, it can be seen that the variable universe fuzzy fractional - order PID control algorithm proposed by the present invention has obvious advantages in the peak time index, overshoot index, and steady - state error index without significantly sacrificing the rise time and settling time.

[0026] Table 7 Performance Index Analysis Table

[0027] Based on the same inventive concept, an embodiment of this application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned variable universe fuzzy fractional - order PID control method for the NOx concentration in flue gas denitrification.

[0028] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the variable universe fuzzy fractional-order PID control method for the NOx concentration in flue gas denitrification described above are implemented.

[0029] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0030] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0031] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0032] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0033] The above embodiments are only used to illustrate the technical idea of the present invention, and the protection scope of the present invention cannot be limited thereby. Any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the present invention.

Claims

1. A variable universe fuzzy fractional-order PID control method for flue gas denitrification NOx concentration, characterized in that: The steps include: Step 1, designing a fractional-order PID controller for the flue gas denitrification system to control the NOx concentration output by the flue gas denitrification system, and using an improved crowd search algorithm to determine the initial values ​​of the fractional-order PID controller parameters; Step 2: Obtain the NOx mass concentration error of the flue gas denitrification system and error rate of change , and used as the input variable of the fuzzy controller, the change of the fractional-order PID controller parameters is used as the output variable of the fuzzy controller, and the fuzzy rules of the fuzzy controller output variable are determined; Step 3: Select the scaling factor construction method based on fuzzy reasoning form and design the variable domain fuzzy system. and As the input of the variable universe fuzzy system, the output of the variable universe fuzzy system and As the scaling factor of the fuzzy controller input variable, the output of the variable universe fuzzy system As the expansion factor of the output variable of the fuzzy controller, the input and output domains of the fuzzy controller are processed in a variable domain; Step 4, combining the change of the fractional-order PID controller parameters output by the fuzzy controller after variable domain processing and the initial values ​​of the fractional-order PID controller parameters to obtain the fractional-order PID controller parameters, thereby realizing adaptive control of the NOx concentration output by the flue gas denitrification system.

2. The variable universe fuzzy fractional order PID control method for flue gas denitrification NOx concentration according to claim 1 is characterized in that: In step 1, the fractional-order PID controller is expressed as: ， In the formula, For a PID controller that combines fractional-order characteristics, is the proportionality coefficient, is the integration coefficient, is the differential coefficient, is the integration order, is the differential order, is a complex frequency domain variable; The improved crowd search algorithm is based on the crowd search algorithm. In each iteration, the searched individuals are sorted from small to large according to the fitness value, and the individual position with the largest fitness value is randomly mutated and then replaced with the individual position with the smallest fitness value. Initial values ​​of fractional-order PID controller parameters, including: initial value of proportional coefficient , initial value of integral coefficient and initial value of differential coefficient .

3. The variable universe fuzzy fractional order PID control method for flue gas denitration NOx concentration according to claim 1 is characterized in that: In step 2, the input and output variables of the fuzzy controller are fuzzified, and the fuzzy sets of the input and output variables are , and They represent negative large, negative medium, negative small, zero, positive small, positive medium and positive large respectively, and the membership functions all adopt triangular functions; The output variables of the fuzzy controller include the fractional-order PID proportional coefficient change , integral coefficient change and the change in differential coefficient ;in, The fuzzy rules are shown in Table 1: Table 1 ， The fuzzy rules are shown in Table 2: Table 2 ， The fuzzy rules are shown in Table 3: Table 3 。 4. The variable universe fuzzy fractional order PID control method for flue gas denitration NOx concentration according to claim 1 is characterized in that: In step 3, the input and output variables of the variable universe fuzzy system are fuzzified. and The fuzzy sets of , and They represent negative large, negative medium, negative small, zero, positive small, positive medium and positive large respectively; and The fuzzy sets of , and They represent slight compression, moderate compression, basically unchanged, and slight expansion, respectively. The fuzzy set is , and They represent large compression, moderate compression, slight compression, unchanged, slight expansion, moderate expansion and large expansion respectively; the membership functions all adopt triangular functions; in, The fuzzy rules are shown in Table 4: Table 4 ， The fuzzy rules are shown in Table 5: Table 5 ， The fuzzy rules are shown in Table 6: Table 6 ， The input and output domains of the fuzzy controller are transformed into: ， In the formula, Input variables for the fuzzy controller The initial domain of Input variables for the fuzzy controller The initial domain of Output variables for fuzzy controller , and The initial domain of ; , and The domain after the variable domain is adjusted.

5. The variable universe fuzzy fractional order PID control method for flue gas denitration NOx concentration according to claim 1 is characterized in that: In step 4, the expression of the fractional-order PID controller parameters is: ， In the formula, , and is the initial value of the fractional-order PID controller parameters, , and is the change in the parameters of the fractional-order PID controller output by the fuzzy controller after variable domain processing, , and are the parameters of the modified fractional-order PID controller.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the computer program, the steps of the variable domain fuzzy fractional order PID control method for flue gas denitrification NOx concentration as described in any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the variable domain fuzzy fractional order PID control method for flue gas denitrification NOx concentration as claimed in any one of claims 1 to 5 are implemented.

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