CN114721253A - Heating furnace temperature fractional order PID control system and method based on artificial bee colony algorithm - Google Patents
Heating furnace temperature fractional order PID control system and method based on artificial bee colony algorithm Download PDFInfo
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Abstract
The invention discloses a heating furnace temperature fractional order PID control system and method based on an artificial bee colony algorithm, belonging to the technical field of furnace and kiln combustion control in a steel rolling industry. The invention is improved on the basis of the traditional PID control, and a fractional order thought is introduced for control; in addition, in order to obtain a better control effect, each parameter of the fractional order PID is optimized by utilizing the artificial bee colony, so that the stable control of the temperature of the hearth of the heating furnace is realized; compared with the traditional PID optimization control, the method can control the temperature of the furnace chamber of the heating furnace in real time, effectively solves the problems of large inertia, large lag and slow response existing in the current temperature control of the heating furnace, improves the stability of temperature regulation, shortens the regulation response time, realizes the high-efficiency regulation and control of the furnace temperature, and is worthy of popularization and application.
Description
Technical Field
The invention relates to the technical field of combustion control of furnaces and kilns in steel rolling industry, in particular to a heating furnace temperature fractional order PID control system and method based on an artificial bee colony algorithm.
Background
As an important component of the steel rolling process, the heating furnace is continuously producing and heating hot air. The steel rolling heating furnace is a large energy consumption user in the steel rolling process, and the fuel consumption of the heating furnace accounts for 80-85% of the total energy medium consumption in the process. In addition, the air temperature provided by the heating furnace directly influences the thermal efficiency of the chemical reaction of the subsequent steel rolling process, so that the continuous research, exploration and application of a new control scheme on the heating furnace is a necessary trend at present.
The hot air is mainly generated from the combustion reaction of air and oxygen, and the production efficiency of the hot air depends on the air-fuel ratio in the heating furnace, namely the ratio of two reactants. Besides the hardware structure of the heating furnace, the adjustment control is mostly carried out through an optimization control algorithm at present, the long-term constant temperature in the heating furnace can be realized through the adjustment of the input gas quantity, the guarantee is provided for the full and efficient heat supply of a steel rolling process production line, and considerable economic benefits are created for enterprises.
The existing heating furnace combustion technology mainly comprises the steps of calculating input and output heat values or flow, adjusting and introducing RBF neural network learning model control, the first method has high hardware requirements, has high requirements on precision of equipment such as a heat value instrument and a thermometer, is still in a research stage after a long time appears, and is rarely tested or put into use by enterprises at present.
At present, heating furnace equipment of a plurality of steel rolling enterprises in China is not updated, the technology is backward, industrial production is still carried out by adopting a manual control and adjustment method, the precision can not be ensured, a large amount of manpower and material resources are needed for maintaining the operation of the heating furnace, the experience of personnel is different, and the method has great limitation. Therefore, a heating furnace temperature fractional order PID control system based on an artificial bee colony algorithm is provided.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the heating furnace temperature fractional order PID control system based on the artificial bee colony algorithm is provided, a temperature sensor is selected as a data acquisition module, PID is selected as a main control system, the concept of fractional order is integrated into a PID controller, and an integral link and a differential link are expanded to fractional order integral and differential; meanwhile, an artificial bee colony algorithm, namely an ABC algorithm, is introduced, and each parameter of the fractional order PID controller is optimized to obtain a relatively optimal parameter, so that an optimal control result is obtained.
The invention solves the technical problems through the following technical scheme, and the invention comprises a temperature sensor, an A/D converter, a fractional order PID controller after artificial bee colony optimization, a D/A converter and a control actuator; the current temperature in the heating furnace is monitored through the temperature sensor, a monitoring result is converted into a digital signal through an A/D converter, a digital control signal is sent out through a fractional order PID controller optimized by an artificial bee colony, the digital control signal is converted into an analog control signal through the D/A converter, a control actuator controls the gas inflow of the heating furnace according to the analog control signal, and then closed-loop control is carried out on the temperature in the heating furnace.
The invention also provides a heating furnace temperature fractional order PID control method based on the artificial bee colony algorithm, the control system can realize the control of the temperature in the heating furnace, effectively relieves the problems of slow regulation, large temperature overshoot and the like of the traditional control method, has the characteristics of good control effect and long-term linearity maintenance, and comprises the following steps:
s1: collecting variable data of a combustion process of a heating furnace, and establishing a mathematical model of the combustion process;
s2: the heating furnace is suspended from running, and the original PID controller is replaced by a fractional order PID controller;
s3: introducing an artificial bee colony algorithm, selecting parameters of the fractional order PID controller, and inputting the parameters into the fractional order PID controller after obtaining the optimal parameters;
s4: when the temperature in the heating furnace reaches relative stability, namely the difference value between the actual temperature in the furnace and the expected temperature is in a set range, the control system suspends the action, namely keeps the dynamic stability and does not adjust temporarily;
s5: and (4) continuously heating, repeating the step S3 to the step S4, and carrying out closed-loop control on the heating process.
Further, in the step S1, the heating combustion process variable data includes: a controlled quantity and a controlled quantity; the control quantity comprises input coal gas quantity; the controlled quantity comprises the temperature in the heating furnace.
Further, in the step S1, the process of establishing the mathematical model of the combustion process includes the following steps:
s11: according to the variable data of the combustion process, the transfer function is simplified as follows:
wherein: k1The gas inlet flow coefficient is; k2Is a heating temperature conversion coefficient; t is a unit of1The gas inlet time is the gas inlet time; t is2The heating time in the furnace is; t is a function argument;
s12: obtaining a relational expression of input and output of a control system:
wherein: r (t) is a system control input; p (t) refers to fractional order PID controller effects; y (t) is the system control output.
Further, in the step S2, the control transfer function of the original PID controller:
wherein: k ispIs a proportionality coefficient; kiIs an integral coefficient; kdIs a differential coefficient.
Further, in the step S2, the control transfer function of the fractional order PID controller is:
wherein,
is an integral term; t is tμIs a differential term; kpIs a proportionality coefficient; kiIs an integral coefficient; kdIs a differential coefficient.
Further, in the step S3, the step of selecting the parameters of the fractional PID controller to obtain the optimal parameters and entering the fractional PID controller includes the following steps:
s31: initializing a population, and initializing PID parameter values to be optimized and values of related variables of an artificial bee colony algorithm;
s32: employing bees to mine around the current food source while evaluating the new solutions generated;
s33: calculating the probability of each food source being selected by using a selection probability formula, selecting the food source according to the probability value by the follower bees, and further exploring the vicinity of the selected food source;
s34: if no better food source can be found after limited search, the current bee colony is considered to enter a local extreme value, and the reconnaissance bees randomly generate a new food source to replace the position;
s35: and judging whether the algorithm meets the stop condition, if not, executing the process in step S32 in a repeated cycle, and if so, finishing the process and outputting a final result.
Further, in the step S31, the PID parameter value includes Kp、Ki、Kdλ, μ; the values of the related variables of the artificial bee colony algorithm comprise the population size and the iteration number.
Compared with the prior art, the invention has the following advantages: the heating furnace temperature fractional order PID control system based on the artificial bee colony algorithm is improved on the basis of the traditional PID control, and a fractional order idea is introduced for control; in addition, in order to obtain a better control effect, each parameter of the fractional order PID is optimized by utilizing the artificial bee colony, so that the stable control of the temperature of the hearth of the heating furnace is realized; compared with the traditional PID optimization control, the method can control the temperature of the furnace chamber of the heating furnace in real time, effectively solves the problems of large inertia, large lag and slow response existing in the current temperature control of the heating furnace, improves the stability of temperature regulation, shortens the regulation response time, realizes the high-efficiency regulation and control of the furnace temperature, and is worthy of popularization and application.
Drawings
FIG. 1 is a schematic structural diagram of a furnace temperature control system of a heating furnace according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of a furnace temperature control method of a heating furnace according to a first embodiment of the present invention.
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
Example one
As shown in fig. 1 and 2, the present embodiment provides a technical solution: a heating furnace temperature control system combining an artificial bee colony algorithm and a fractional order PID comprises: the system comprises a temperature sensor, an A/D converter, a fractional order PID controller, a D/A converter and a control actuator. The heating furnace temperature control system monitors the current heating furnace hearth temperature (namely the temperature in the furnace) through a temperature sensor, the result is converted into a digital signal through an A/D converter, and the gas inflow of the system gas is controlled through a fractional order PID controller optimized by a manual bee colony, so that the whole heating furnace temperature control system is formed.
The embodiment also provides a heating furnace temperature control method combining the artificial bee colony algorithm and the fractional PID, which comprises the following steps:
step 1, initializing parameters of an original traditional PID controller (namely the original PID controller of the invention) of a heating furnace, wherein the parameters comprise data such as target temperature, rated furnace pressure, various coefficients of the controller, variable frequency parameters of a blower, air-fuel ratio and the like;
step 2, collecting variable data of a combustion process of the heating furnace, and establishing a mathematical model of the combustion process;
step 3, suspending the operation of the heating furnace, and replacing the original traditional PID controller with a fractional order PID controller;
step 4, introducing an Artificial Bee Colony (ABC) algorithm, selecting fractional order PID controller parameters, and substituting the relative optimal parameters into the PID controller;
step 5, setting the desired temperature to be 1200 ℃, the deviation to be +/-4.8 ℃, and controlling a system to pause, namely to keep dynamic stability and not to adjust temporarily;
step 6, continuously heating, repeating the step 4 to the step 5, and carrying out closed-loop control on the heating process;
in the present embodiment, the heating combustion process variable data in step 2 includes: a controlled quantity and a controlled quantity; the control quantity comprises input coal gas quantity; the controlled quantity comprises the temperature in the heating furnace.
In this embodiment, the process of establishing the mathematical model of the combustion process in step 2 includes the following steps:
step 2.1, according to the variable data of the combustion process, the transfer function can be simplified as follows:
wherein: k1The gas inlet flow coefficient is; k2Is a heating temperature conversion coefficient; t is1The gas inlet time is the gas inlet time; t is2The heating time in the furnace is set; t is a function independent variable and has no specific meaning;
step 2.2, according to the design idea of the invention, obtaining a relational expression about input and output:
wherein: r (t) is a system control input; p (t) refers herein to a (fractional order) PID controller effect; y (t) is the system control output.
In the embodiment, the conventional PID controller mentioned in step 3 corresponds to the control method which is one of the most classical and mature control strategies, and has wide application in industries such as machinery, chemistry, metallurgy and the like.
The conventional PID control transfer function is:
wherein: kpIs a proportionality coefficient; kiIs an integral coefficient; kdIs a differential coefficient.
In order to make the design of the controller more flexible, the performance more excellent, and better guarantee the dynamic performance of the system, the concept of fractional order is integrated into the PID controller in this embodiment, and the integral link and the differential link of the integral solution PID controller are expanded to fractional order integral and differential, so as to obtain a brand new PID control transfer function:
wherein,
is an integral term; t is tμIs a differential term; kpIs a proportionality coefficient; kiIs an integral coefficient; kdIs a differential coefficient.
In this embodiment, the step 4 includes introducing an Artificial Bee Colony (ABC) algorithm, selecting parameters of the fractional PID controller, and obtaining relatively optimal parameters to enter the fractional PID controller, which includes the following specific steps:
step 4.1, initializing a population, and initializing PID parameter values to be optimized and values of algorithm related variables, wherein the parameter values comprise: kp、Ki、Kdλ, μ; the algorithm related variables comprise population scale, iteration times and the like;
step 4.2, hiring bees to mine around the current food source and evaluate the generated new solution;
4.3, calculating the probability of each food source being selected by using a selection probability formula, selecting the food source according to the probability value by the following bees, and further exploring the vicinity of the selected food source;
4.4, if a better food source cannot be found after limited search, considering that the current bee colony enters a local extreme value in an optimized manner, and randomly generating a new food source to replace the position by the detection bee;
and 4.5, judging whether the algorithm meets the stop condition, if not, executing the algorithm in the step 4.2 in a repeated cycle, and if so, finishing the process and outputting a final result.
The control system in this embodiment is used as a measurement control system, and besides data such as adjustment time and overshoot, an error integral criterion is also an important measure for measuring the performance of the control system. In order to verify the advantages and disadvantages of the method, the time multiplied by the absolute error integral criterion, namely an ITAE index is selected as a reference:
wherein, J (ITAE)i) Is the integral of the absolute value reflecting the error with time;
the corresponding fitness is:
wherein: fit refers to the fitness function.
Example two
For comparing and optimizing the performance, in this embodiment, the same PID parameters are compared by introducing ABC (artificial bee colony) and PSO (particle swarm optimization) algorithms using Matlab program simulation software, and Kp、KiAnd KdThe search space ranges are respectively set to be 0-40, 0-10 and 0-2, the initial population scale is defined to be 40, the maximum iteration number is set to be 100, the inertia weight of the particle swarm algorithm is set to be 0.6, and the acceleration coefficient c is set to be1＝c22, D3, the maximum limit search time is 120, and the two algorithms are run 10 times each, comparing the fitness value and the simulated step response.
By comparing the simulation graphs, it can be found that: the dynamic characteristics of the solution obtained by PSO algorithm optimization are better, but the solution corresponding to the ABC algorithm is better in steady-state performance.
In order to verify the effectiveness of control, the invention simulates an output curve chart through Matlab program simulation software, and the specific steps are as follows:
step 4.6, selecting a step signal as reference input, and setting the simulation time to be 10 s;
4.7, setting and adjusting PID parameters until the output curve chart is similar to the original control chart;
step 4.8, introducing a manual bee colony to optimize each parameter of the PID, wherein the specific operation is as described in the step 4.1-4.5;
and 4.9, controlling the system to continuously operate and outputting an effect graph.
In order to verify the application effect of the method in practical engineering, the method is introduced into a 1780mm unit walking beam type heating furnace of a certain mill to be verified, and the result shows that the method can realize the control of the temperature in the heating furnace, effectively relieves the problems of slow adjustment, large temperature overshoot and the like of the traditional control method, and has the characteristics of good control effect and long-term linearity maintenance.
In summary, the fractional order PID control system of the heating furnace temperature based on the artificial bee colony algorithm of the above embodiment is improved on the basis of the traditional PID control, and the fractional order idea is introduced for control; in addition, in order to obtain a better control effect, each parameter of the fractional order PID is optimized by utilizing the artificial bee colony, so that the stable control of the temperature of the hearth of the heating furnace is realized; compared with the traditional PID optimization control, the method can control the temperature of the furnace chamber of the heating furnace in real time, effectively solves the problems of large inertia, large lag and slow response existing in the current temperature control of the heating furnace, improves the stability of temperature regulation, shortens the regulation response time, realizes the high-efficiency regulation and control of the furnace temperature, and is worthy of popularization and application.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (8)
1. Heating furnace temperature fractional order PID control system based on artifical bee colony algorithm, its characterized in that includes: the system comprises a temperature sensor, an A/D converter, a fractional order PID controller after artificial bee colony optimization, a D/A converter and a control actuator; the current temperature in the heating furnace is monitored through the temperature sensor, a monitoring result is converted into a digital signal through an A/D converter, a digital control signal is sent out through a fractional order PID controller optimized by an artificial bee colony, the digital control signal is converted into an analog control signal through the D/A converter, a control actuator controls the gas inflow of the heating furnace according to the analog control signal, and then closed-loop control is carried out on the temperature in the heating furnace.
2. The heating furnace temperature fractional order PID control method based on the artificial bee colony algorithm is characterized in that the control system according to claim 1 is adopted to control the temperature in the heating furnace, and the method comprises the following steps:
s1: collecting variable data of a combustion process of a heating furnace, and establishing a mathematical model of the combustion process;
s2: suspending the heating furnace to run, and replacing the original PID controller with a fractional order PID controller;
s3: introducing an artificial bee colony algorithm, selecting parameters of the fractional order PID controller, and inputting the parameters into the fractional order PID controller after obtaining the optimal parameters;
s4: when the temperature in the heating furnace reaches relative stability, namely the difference value between the actual temperature in the furnace and the expected temperature is in a set range, the control system suspends the action, namely keeps the dynamic stability and does not adjust temporarily;
s5: and (4) continuously heating, repeating the step S3 to the step S4, and carrying out closed-loop control on the heating process.
3. The heating furnace temperature fractional order PID control method based on the artificial bee colony algorithm according to claim 2, characterized in that: in the step S1, the heating combustion process variable data includes: a controlled quantity and a controlled quantity; the control quantity comprises the input coal gas quantity; the controlled quantity comprises the temperature in the heating furnace.
4. The heating furnace temperature fractional order PID control method based on the artificial bee colony algorithm according to claim 3, characterized in that: in step S1, the process of creating the mathematical model of the combustion process includes the following steps:
s11: according to the variable data of the combustion process, the transfer function is simplified as follows:
wherein: k1The gas inlet flow coefficient is; k2Is a heating temperature conversion coefficient; t is1The gas inlet time is the gas inlet time; t is a unit of2The heating time in the furnace is; t is a function argument;
s12: obtaining a relational expression of input and output of a control system:
wherein: r (t) is a system control input; p (t) refers to fractional order PID controller effects; y (t) is the system control output.
5. The heating furnace temperature fractional order PID control method based on the artificial bee colony algorithm according to claim 4, characterized in that: in step S2, the control transfer function of the original PID controller:
wherein: kpIs a proportionality coefficient; kiIs an integral coefficient; kdIs a differential coefficient.
6. The heating furnace temperature fractional order PID control method based on the artificial bee colony algorithm according to claim 5, characterized in that: in step S2, the control transfer function of the fractional order PID controller is:
wherein,
is an integral term; t is tμIs a differential term; k ispIs a proportionality coefficient; kiIs an integral coefficient; kdIs a differential coefficient.
7. The heating furnace temperature fractional order PID control method based on the artificial bee colony algorithm according to claim 6, characterized in that: in the step S3, the step of selecting the parameters of the fractional order PID controller to obtain the optimal parameters and then entering the fractional order PID controller includes the following steps:
s31: initializing a population, and initializing PID parameter values to be optimized and values of related variables of an artificial bee colony algorithm;
s32: employing bees to mine around the current food source while evaluating the new solutions generated;
s33: calculating the probability of each food source being selected by using a selection probability formula, selecting the food source by the follower bees according to the probability value, and further exploring the vicinity of the selected food source;
s34: if a better food source cannot be found after limited search, the current bee colony is considered to enter a local extreme value, and at the moment, the reconnaissance bees randomly generate a new food source to replace the position;
s35: and judging whether the algorithm meets the stop condition, if not, executing the process in step S32 in a repeated cycle, and if so, finishing the process and outputting a final result.
8. The heating furnace temperature fractional order PID control method based on the artificial bee colony algorithm according to claim 7, characterized in that: in the step S31, the PID parameter values include Kp、Ki、Kdλ, μ; the values of the related variables of the artificial bee colony algorithm comprise the population size and the iteration number.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115629537A (en) * | 2022-12-22 | 2023-01-20 | 南京净环热冶金工程有限公司 | Heating furnace combustion control method and system based on subgroup improved particle swarm optimization PID |
| CN118276435A (en) * | 2024-06-03 | 2024-07-02 | 安徽大学 | Polymerization reaction kettle improved PID control method and system oriented to strong temperature constraint |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115629537A (en) * | 2022-12-22 | 2023-01-20 | 南京净环热冶金工程有限公司 | Heating furnace combustion control method and system based on subgroup improved particle swarm optimization PID |
| CN118276435A (en) * | 2024-06-03 | 2024-07-02 | 安徽大学 | Polymerization reaction kettle improved PID control method and system oriented to strong temperature constraint |
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