CN120255328A - Boiler control method, system, medium, equipment and device based on fuzzy neural network - Google Patents
Boiler control method, system, medium, equipment and device based on fuzzy neural network Download PDFInfo
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- CN120255328A CN120255328A CN202410008785.0A CN202410008785A CN120255328A CN 120255328 A CN120255328 A CN 120255328A CN 202410008785 A CN202410008785 A CN 202410008785A CN 120255328 A CN120255328 A CN 120255328A
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Abstract
The invention provides a boiler control method, a system, a medium, equipment and a device based on a fuzzy neural network, wherein the method comprises the following steps of acquiring and processing operation data of a gas boiler; training a fuzzy neural network model based on the processed gas boiler operation data, wherein the fuzzy neural network model is used for realizing boiler control, and performing boiler control performance simulation based on the trained fuzzy neural network model so as to obtain an optimal control scheme of the gas boiler. The fuzzy neural network-based boiler control method, system, medium, equipment and device can improve the thermal efficiency of the boiler and stabilize the combustion of the boiler, and have profound significance for realizing the control intellectualization of the boiler system and reducing the redundancy of the system.
Description
Technical Field
The invention belongs to the technical field of intelligent control, and particularly relates to a boiler control method, system, medium, equipment and device based on a fuzzy neural network.
Background
The gas boiler plant is a large, complex control object. The boiler system has several input parameters, such as water feeding amount, fuel input amount, air feeding amount, air introducing amount, etc. and several output parameters, such as drum pressure, hearth negative pressure, steam temperature, pressure, etc. The close association and interaction between the input and output parameters is a complex nonlinear problem. The automatic control system of the gas boiler mainly aims at maintaining the water level of a boiler drum, the combustion state and the outlet steam temperature and pressure of a superheater within a specified range. The traditional gas boiler control adopts a classical PID algorithm, system oscillation and overshoot occur at the same time, and a method for controlling the boiler by using a neural network is still immature. The boiler operation parameters are gradually expanded along with the production scale of the boiler industry, and the operation stability of the gas boiler must be ensured.
Therefore, how to solve the above-mentioned problems becomes a problem to be solved in the art.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the present invention aims to provide a method, a system, a medium, a device and a device for controlling a boiler based on a fuzzy neural network, which solve the problems of tight coupling of parameters in multiple loops, obvious hysteresis in parameter adjustment, strong interference among parameters, etc. in the adjustment of the existing boiler system, and further improve the stability of the operation of the boiler and the thermal efficiency of the operation of the boiler.
In a first aspect, the present invention provides a fuzzy neural network-based boiler control method, the method comprising the steps of:
acquiring and processing operation data of the gas boiler;
Training a fuzzy neural network model based on the processed gas boiler operation data, wherein the fuzzy neural network model is used for realizing boiler control;
and performing boiler control performance simulation based on the trained fuzzy neural network model to obtain an optimal control scheme of the gas boiler.
In one implementation manner of the first aspect, the acquiring and processing the operation data of the gas boiler includes the following steps:
acquiring operation data of the gas boiler, wherein the operation data at least comprises water supply part operation parameters, combustion part operation parameters and superheater operation parameters of the gas boiler at different external temperatures and different times;
And processing the gas boiler operation data by adopting a mean value interpolation method to obtain the processed gas boiler operation data.
In one implementation form of the first aspect, training the fuzzy neural network based on the processed gas boiler operation data comprises the steps of:
dividing the processed gas boiler operation data into a training set and a testing set, wherein the training set is used for training the fuzzy neural network model, and the testing set is used for simulating and verifying the boiler control performance;
Training the fuzzy neural network model based on the training set;
And carrying out simulation verification on the trained fuzzy neural network model based on the test set, and selecting the parameter with the best performance as the trained fuzzy neural network model.
In one implementation manner of the first aspect, performing boiler control performance simulation based on the trained fuzzy neural network model to obtain an optimal control scheme of the gas boiler includes the following steps:
inputting actual measurement operation data and target constraint conditions of the gas boiler into the trained fuzzy neural network model;
and obtaining the optimal control scheme of the gas boiler output by the trained fuzzy neural network model.
In one implementation manner of the first aspect, the fuzzy neural network model adopts a BP neural network model of a T-S fuzzy theory.
In a second aspect, the invention provides a boiler control system based on a fuzzy neural network, which comprises a data acquisition module, a data analysis and training module and an actual operation module;
the data acquisition module is used for acquiring and processing the operation data of the gas boiler;
The data analysis and training module is used for training a fuzzy neural network based on the processed gas boiler operation data, and the fuzzy neural network model is used for realizing boiler control;
The actual operation module is used for carrying out boiler control performance simulation based on the trained fuzzy neural network model so as to obtain an optimal control scheme of the gas boiler.
In a third aspect, the present invention provides an electronic device comprising a processor and a memory;
the memory is used for storing a computer program;
The processor is used for executing the computer program stored in the memory so as to enable the electronic equipment to execute the boiler control method based on the fuzzy neural network.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by an electronic device, implements the above-described fuzzy neural network-based boiler control method.
In a fifth aspect, the present invention provides a fuzzy neural network-based boiler control apparatus, including a data acquisition device and an electronic device for controlling a boiler based on a fuzzy neural network;
The data acquisition equipment is used for acquiring the operation data of the gas boiler and providing the operation data of the gas boiler to the electronic equipment based on the fuzzy neural network for boiler control.
In one implementation manner of the first aspect, the data acquisition equipment comprises a water supply data acquisition device, a combustion part data acquisition device and a superheater data acquisition device, wherein the water supply data acquisition device comprises a boiler water supply temperature sensor for monitoring boiler water supply temperature, a boiler water supply flow sensor for monitoring boiler water supply flow and a drum water level sensor for monitoring drum water level, the combustion part data acquisition device comprises a fuel inlet flow sensor for monitoring fuel inlet flow, a fuel inlet temperature sensor for monitoring fuel inlet temperature, an air inlet flow sensor for monitoring air inlet flow and an air inlet temperature sensor for monitoring air inlet temperature, and the superheater data acquisition device comprises a temperature sensor for monitoring superheater outlet steam temperature, a superheater outlet steam pressure sensor for monitoring superheater outlet steam pressure and a superheater outlet steam pressure sensor for monitoring superheater outlet steam pressure.
As described above, the fuzzy neural network-based boiler control method, system, medium, equipment and device provided by the invention have the following beneficial effects:
The method, the system, the medium, the equipment and the device for controlling the boiler based on the fuzzy neural network control the operation state of the boiler by adopting a mode of combining a fuzzy theory and the neural network, not only solve the problems that in the adjustment of the existing boiler system, parameters in a plurality of loops are mutually influenced and tightly coupled, obvious hysteresis exists in parameter adjustment, strong interference exists among all the parameters, and the like. The stability of the operation of the boiler can be improved, and the operating thermal efficiency of the boiler is improved. The invention aims at intelligent upgrading of a traditional boiler control system. A large number of simulation results show that for the control object of the gas boiler, which is nonlinear, closely related to multiple inputs and outputs and is difficult to establish an accurate mathematical model to obtain a numerical analysis solution, a better control effect can be obtained by using a fuzzy neural network model, and the operation efficiency, the combustion stability and the self-adaptive capacity of the system of the boiler are all improved. The model can be used in the control fields of coal-fired boilers, industrial gas boilers, waste heat boilers and the like of power plants, and has profound significance for realizing the control intellectualization of boiler systems and reducing the redundancy of the systems.
Drawings
FIG. 1 is a flow chart of a fuzzy neural network based boiler control method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a fuzzy neural network based boiler control system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an electronic device according to an embodiment of the invention;
Fig. 4 is a schematic structural diagram of a fuzzy neural network based boiler control device according to an embodiment of the present invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
The following describes the technical solution in the embodiment of the present invention in detail with reference to the drawings in the embodiment of the present invention.
As shown in fig. 1, in an embodiment, the fuzzy neural network-based boiler control method of the present invention includes steps S11-S13.
And S11, acquiring and processing the operation data of the gas boiler.
The method comprises the steps of obtaining operation data of a gas boiler, wherein the operation data at least comprise water supply part operation parameters, combustion part operation parameters and superheater operation parameters of the gas boiler at different external temperatures and different times.
The operation parameters of different working states of the gas boiler are collected at different external temperatures and different times, so that the diversity of sample data is ensured, and the trained neural network model has better generalization performance.
And processing the gas boiler operation data by adopting a mean value interpolation method, namely filling and replacing missing data and error data, and obtaining the processed gas boiler operation data.
And step S12, training a fuzzy neural network based on the processed gas boiler operation data, wherein the fuzzy neural network model is used for realizing boiler control.
Specifically, the processed gas boiler operation data are divided into a training set and a testing set (80% is used as the training set and 20% is used as the testing set), the training set is used for training the fuzzy neural network model, the testing set is used for simulating and verifying the boiler control performance, and the fuzzy neural network model adopts a BP neural network model of a T-S fuzzy theory.
And training the fuzzy neural network model based on the training set, and training a plurality of fuzzy neural network models according to different data combinations.
And carrying out simulation verification on the trained fuzzy neural network model based on the test set, and selecting the parameter with the best performance as the trained fuzzy neural network model, namely selecting the model with the smallest error between the predicted value and the actual value as the fuzzy neural network prediction control algorithm model of the gas boiler. Furthermore, the fuzzy neural network predictive control algorithm model of the gas boiler system adopts the fuel calorific value, the fuel inlet flow, the fuel inlet temperature, the air inlet flow and the superheater outlet steam flow as input units of the neural network, and adopts the superheater outlet steam temperature and the superheater outlet steam pressure as output units of the neural network.
And step S13, performing boiler control performance simulation based on the trained fuzzy neural network model to obtain an optimal control scheme of the gas boiler.
Specifically, the actual measurement operation data of the gas boiler and target constraint conditions are input into the trained fuzzy neural network model, and the optimal control scheme of the gas boiler, which is output by the trained fuzzy neural network model, is obtained. And verifying the trained fuzzy neural network model by using the actually measured operation data, inputting constraint conditions of a target after verification is successful, and calculating to obtain an optimal operation scheme of the gas boiler through the fuzzy neural network model.
The protection scope of the boiler control method based on the fuzzy neural network according to the embodiment of the invention is not limited to the execution sequence of the steps listed in the embodiment, and all the schemes realized by the steps of increasing and decreasing and step replacement in the prior art according to the principles of the invention are included in the protection scope of the invention.
The embodiment of the invention also provides a fuzzy neural network-based boiler control system, which can realize the fuzzy neural network-based boiler control method, but the realizing device of the fuzzy neural network-based boiler control system comprises but is not limited to the structure of the fuzzy neural network-based boiler control system listed in the embodiment, and all structural variations and substitutions of the prior art according to the principles of the invention are included in the protection scope of the invention.
As shown in fig. 2, in an embodiment, the fuzzy neural network based boiler control system of the present invention includes a data acquisition module 21, a data analysis and training module 22, and an actual operation module 23.
The data acquisition module 21 is used for acquiring and processing the operation data of the gas boiler;
The data analysis and training module 22 is used for training a fuzzy neural network model based on the processed gas boiler operation data, and the fuzzy neural network model is used for realizing boiler control;
The actual operation module 23 is configured to perform boiler control performance simulation based on the trained fuzzy neural network model, so as to obtain an optimal control scheme of the gas boiler.
The data acquisition module 21 and the data analysis are connected with the training module 22, and the actual operation module 23 and the data analysis are connected with the training module.
The data acquisition module 21 comprises a water supply data acquisition unit, a combustion part data acquisition unit and a superheater data acquisition unit, wherein the water supply data acquisition unit comprises a water supply temperature sensor, a boiler water supply flow sensor and a drum water level sensor, the combustion part data acquisition unit comprises a fuel inlet flow sensor, a fuel inlet temperature sensor, an air inlet flow sensor and an air inlet temperature sensor, and the superheater data acquisition unit comprises a superheater outlet steam temperature sensor, a superheater outlet steam pressure sensor and a superheater outlet steam flow sensor.
The system comprises a boiler water supply temperature sensor, a boiler water supply flow sensor, a steam drum water level sensor, a data analysis and training module 22, a data processing unit and a control unit, wherein the water supply temperature sensor is used for monitoring the boiler water supply temperature, the boiler water supply flow sensor is used for monitoring the boiler water supply flow, and the steam drum water level sensor is used for monitoring the steam drum water level;
The fuel charging flow sensor is used for monitoring the fuel charging flow, the fuel charging temperature sensor is used for monitoring the fuel charging temperature, the air charging flow sensor is used for monitoring the air charging flow, and the air charging temperature sensor is used for monitoring the air charging temperature;
The superheater outlet steam temperature sensor is used for monitoring the superheater outlet steam temperature, the superheater outlet steam pressure sensor is used for monitoring the superheater outlet steam pressure, the superheater outlet steam flow sensor is used for monitoring the superheater outlet steam flow, and data of the superheater outlet steam temperature sensor, the superheater outlet steam pressure sensor and the superheater outlet steam flow sensor are transmitted to a data processing unit of the data analysis and training module 22.
The data analysis and training module 22 includes a data processing unit, a neural network input unit, and a neural network output unit. The data processing unit is used for processing the data transmitted by the data acquisition module 21, filling and replacing missing data and error data by using a mean value interpolation method, and transmitting input data and output data of the boiler system to the neural network input unit and the neural network output unit respectively, wherein the neural network input unit is used for receiving the boiler system input data transmitted by the data processing unit for training, and the neural network output unit is used for receiving the boiler system output data transmitted by the data processing unit for training.
The actual operation module 23 is used for verifying the neural network model trained by the data analysis and training module 22 and simulating the operation performance of the subsequent boiler.
In an embodiment, the data acquisition module 21 acquires the boiler operation data, sends the boiler operation data to the data processing unit, after the data processing unit processes the data, transmits a part of the data as a training set to the neural network input unit and the neural network output unit for training the neural network, and after the training is completed, the neural network model is tested by using the test set, the actual operation module 23 is used for verifying the neural network model, after the verification is successful, the constraint condition of a target is input, and the optimal operation scheme of the gas boiler is obtained through calculation of the fuzzy neural network model.
In the several embodiments provided in the present invention, it should be understood that the disclosed system, apparatus, or method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules/units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple modules or units may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules or units, which may be in electrical, mechanical or other forms.
The modules/units illustrated as separate components may or may not be physically separate, and components shown as modules/units may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules/units may be selected according to actual needs to achieve the objectives of the embodiments of the present invention. For example, functional modules/units in various embodiments of the invention may be integrated into one processing module, or each module/unit may exist alone physically, or two or more modules/units may be integrated into one module/unit.
Those of ordinary skill would further appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The embodiment of the invention also provides a computer readable storage medium. Those of ordinary skill in the art will appreciate that all or part of the steps in a method implementing the above embodiments may be implemented by a program to instruct a processor, where the program may be stored in a computer readable storage medium, where the storage medium is a non-transitory (non-transitory) medium, such as a random access memory, a read only memory, a flash memory, a hard disk, a solid state disk, a magnetic tape (MAGNETIC TAPE), a floppy disk (floppy disk), a compact disk (optical disk), and any combination thereof. The storage media may be any available media that can be accessed by a computer or a data storage device such as a server, data center, or the like that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital video disc (digital video disc, DVD)), or a semiconductor medium (e.g., a Solid State Drive (SSD)), or the like.
The embodiment of the invention also provides electronic equipment. The electronic device includes a processor and a memory.
The memory is used for storing a computer program.
The memory includes a ROM, RAM, magnetic disk, U-disk, memory card, or optical disk, etc. various media that can store program codes.
The processor is connected with the memory and is used for executing the computer program stored in the memory so that the electronic equipment can execute the boiler control method of the fuzzy neural network.
Preferably, the Processor may be a general-purpose Processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network Processor (Network Processor, abbreviated as NP), etc., or may be a digital signal Processor (DIGITAL SIGNAL Processor, abbreviated as DSP), an Application Specific Integrated Circuit (ASIC), a field programmable gate array (Field Programmable GATE ARRAY, abbreviated as FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components.
As shown in fig. 3, the electronic device of the present invention is embodied in the form of a general purpose computing device. The components of the electronic device may include, but are not limited to, one or more processors or processing units 31, a memory 32, and a bus 33 that connects the various system components, including the memory 32 and the processing unit 31.
Bus 33 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Electronic devices typically include a variety of computer system readable media. Such media can be any available media that can be accessed by the electronic device and includes both volatile and nonvolatile media, removable and non-removable media.
Memory 32 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 521 and/or cache memory 322. The electronic device may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 323 may be used to read from and write to non-removable, non-volatile magnetic media (not shown in FIG. 3, commonly referred to as a "hard disk drive"). Although not shown in fig. 3, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be coupled to bus 33 through one or more data medium interfaces. Memory 32 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 324 having a set (at least one) of program modules 3241 can be stored, for example, in memory 32, such program modules 3241 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which can include an implementation of a network environment. Program modules 3241 typically carry out the functions and/or methods of the embodiments described herein.
The electronic device may also communicate with one or more external devices (e.g., keyboard, pointing device, display, etc.), with one or more devices that enable a user to interact with the electronic device, and/or with any device (e.g., network card, modem, etc.) that enables the electronic device to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 34. And the electronic device may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet, via the network adapter 35. As shown in fig. 3, the network adapter 35 communicates with other modules of the electronic device over the bus 33. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with the electronic device, including, but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The embodiment of the invention also provides a boiler control device based on a fuzzy neural network, as shown in fig. 4, in an embodiment, the boiler control device based on the fuzzy neural network of the invention comprises a data acquisition device 41 and an electronic device 42 based on the boiler control of the fuzzy neural network;
the data acquisition device 41 is used for acquiring gas boiler operation data and providing the gas boiler operation data to the electronic device 42 for fuzzy neural network based boiler control.
Specifically, the data collection device 41 includes a feedwater data collector 411, a combustion section data collector 412, a superheater data collector 413;
The water supply data collector 411 comprises a boiler water supply temperature sensor for monitoring the temperature of boiler water supply, a boiler water supply flow sensor for monitoring the flow of boiler water supply and a drum water level sensor for monitoring the drum water level, wherein data collected by the boiler water supply temperature sensor, the boiler water supply flow sensor and the drum water level sensor are provided to the electronic equipment based on the fuzzy neural network for boiler control.
The combustion part data collector 412 comprises a fuel-in flow sensor for monitoring the fuel-in flow, a fuel-in temperature sensor for monitoring the fuel-in temperature, an air-in flow sensor for monitoring the air-in flow, and an air-in temperature sensor for monitoring the air-in temperature, wherein the data collected by the fuel-in flow sensor, the fuel-in temperature sensor, the air-in flow sensor and the air-in temperature sensor are provided to the electronic equipment for controlling the boiler based on the fuzzy neural network.
The superheater data collector 413 includes a temperature sensor for monitoring the superheater outlet steam temperature, a superheater outlet steam pressure sensor for monitoring the superheater outlet steam pressure, and a superheater outlet steam flow sensor for monitoring the superheater outlet steam flow. And the data collected by the temperature sensor of the superheater outlet steam temperature, the superheater outlet steam pressure sensor and the heater outlet steam flow sensor are provided to the electronic equipment of the fuzzy neural network-based boiler control.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (10)
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