CN109858136A - A kind of determination method and apparatus of gas fired-boiler efficiency - Google Patents
A kind of determination method and apparatus of gas fired-boiler efficiency Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000013528 artificial neural network Methods 0.000 claims abstract description 66
- 238000012360 testing method Methods 0.000 claims abstract description 43
- 238000002485 combustion reaction Methods 0.000 claims abstract description 25
- 238000012549 training Methods 0.000 claims abstract description 23
- 238000004088 simulation Methods 0.000 claims abstract description 21
- 239000007789 gas Substances 0.000 claims description 46
- 230000002068 genetic effect Effects 0.000 claims description 12
- 238000004422 calculation algorithm Methods 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 10
- 238000010606 normalization Methods 0.000 claims description 9
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 8
- 239000001301 oxygen Substances 0.000 claims description 8
- 229910052760 oxygen Inorganic materials 0.000 claims description 8
- 238000013461 design Methods 0.000 claims description 6
- 230000005055 memory storage Effects 0.000 claims description 2
- 238000005457 optimization Methods 0.000 abstract description 5
- 238000003860 storage Methods 0.000 description 14
- 238000010586 diagram Methods 0.000 description 10
- 238000012545 processing Methods 0.000 description 10
- 238000004590 computer program Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 6
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- 230000008859 change Effects 0.000 description 2
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Abstract
The invention discloses a kind of determination method and apparatus of gas fired-boiler efficiency, this method comprises: S1: the operating parameter of boiler when obtaining different load；S2: determine artificial neural network outputs and inputs parameter；S3: it is normalized；S4: artificial neural network to be trained is determined；S5: the operating parameter of preset quantity is chosen from the corresponding operating parameter of each load as training sample, remaining operating parameter is as test sample；S6: trained artificial neural network is treated using training sample and is trained to obtain simulation and prediction model；S7: bringing test sample into simulation and prediction model and obtain emulation testing value, judges whether the difference of emulation testing value and actual test value is being preset in allowed band, if so, determining that simulation and prediction model is gas fired-boiler efficiency Model, otherwise executes S5.The present invention establishes boiler combustion efficiency model by artificial neural network, to provide the foundation for the optimization of gas fired-boiler efficiency of combustion.
Description
Technical field
It is the present invention relates to boiler technology field, in particular to a kind of that gas fired-boiler efficiency is optimized based on artificial neural network
Method and apparatus.
Background technique
In recent years, as country is higher and higher to energy conservation and environmental protection requirement, gas fired-boiler is increasingly paid close attention to by everybody.Boiler
Efficiency of combustion in most cases passes through combustion adjustment test organon, number as an important indicator of gas fired-boiler performance
Value analog simulation method is studied, but above two method is limited to field test condition, peopleware, model relevance grade etc.,
Boiler efficiency needs to be further improved.
Summary of the invention
The embodiment of the invention provides a kind of determination method and apparatus of gas fired-boiler efficiency, are built by artificial neural network
Vertical boiler combustion efficiency model, to provide the foundation for the optimization of gas fired-boiler efficiency of combustion.
In a first aspect, the embodiment of the invention provides a kind of determination methods of gas fired-boiler efficiency, this method comprises:
S1: the operating parameter of boiler when obtaining different load；
S2: the input and output parameter of artificial neural network is determined；
S3: the operating parameter of acquisition is normalized；
S4: randomly selecting the operating parameter of preset quantity as training sample from the corresponding operating parameter of each load,
Remaining operating parameter is as test sample；
S5: the artificial neural network to be trained is trained to obtain simulation and prediction model using training sample；
S6: bringing test sample into simulation and prediction model and obtain emulation testing value, and judges emulation testing value and practical survey
Whether the difference of examination value is in preset allowed band, if so, determining that simulation and prediction model is gas fired-boiler efficiency Model, otherwise
Execute step S4.
Preferably,
The detailed process of step S2 includes:
Using gas consumption rate and unit load as the input parameter of artificial neural network, with total air, First air wind
Amount, Secondary Air air quantity, furnace outlet oxygen concentration are joined as affecting parameters using boiler efficiency as the output of artificial neural network
Number.
Preferably,
This method further include:
N1: according to the relationship between boiler controllable operating parameter and combustion characteristics, artificial neural network is determined as 3 layers；
N2: above-mentioned 3 layers of artificial neural network is optimized using genetic algorithm and obtains artificial neural network to be trained.
Preferably,
The detailed process of step N2 includes: to optimize to the weight and threshold value of artificial neural network.
Second aspect, the embodiment of the invention provides a kind of determining device of gas fired-boiler efficiency, which includes: data
Module, parameter determination module, normalization module, sample determining module, network training module and model judgment module are obtained,
In,
The data acquisition module, the operating parameter of boiler when for obtaining different load；
The parameter determination module, for determining the input and output parameter of artificial neural network；
The normalization module, for the operating parameter of acquisition to be normalized；
The sample determining module, the operation for randomly selecting preset quantity from the corresponding operating parameter of each load are joined
Number is used as training sample, remaining operating parameter is as test sample；
The network training module is trained for treating trained artificial neural network using training sample and is emulated
Prediction model；
The model judgment module obtains emulation testing value for bringing test sample into simulation and prediction model, and judges
Whether the difference of emulation testing value and actual test value is in preset allowed band, if so, determining that simulation and prediction model is combustion
Otherwise gas boiler efficiency Model triggers the sample determining module.
Preferably,
The parameter determination module is specifically used for determining using gas consumption rate and unit load as artificial neural network
Parameter is inputted, using total air, First air air quantity, Secondary Air air quantity, furnace outlet oxygen concentration as affecting parameters, with boiler
Output parameter of the efficiency as artificial neural network.
Preferably,
The device further include: network design module, for according to the pass between boiler controllable operating parameter and combustion characteristics
Artificial neural network is determined as 3 layers, and optimizes acquisition to above-mentioned 3 layers of artificial neural network using genetic algorithm by system
Artificial neural network to be trained.
Preferably,
The network design module optimizes the weight and threshold value of artificial neural network.
The third aspect, the embodiment of the invention provides a kind of readable medium, which includes executing instruction, and works as electronics
When executing instruction described in the processor execution of equipment, the electronic equipment is executed obtains gas-fired boiler as described in any in first aspect
The determination method of the efficiency of furnace.
Fourth aspect, the embodiment of the invention provides a kind of electronic equipment, which includes: processor, memory
And bus；The memory is executed instruction for storing, and the processor is connect with the memory by the bus, and institute is worked as
When stating electronic equipment operation, the processor executes the described of memory storage and executes instruction, so that the processor is held
The determination method of row gas fired-boiler efficiency as described in any in first aspect.
Compared with prior art, the present invention at least has the advantages that
The present invention is not only restricted to field test condition, peopleware, model relevance grade etc., passes through Test Data Collecting and people
Artificial neural networks establish boiler combustion efficiency model, to provide the foundation for the optimization of gas fired-boiler efficiency of combustion.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is the present invention
Some embodiments for those of ordinary skill in the art without creative efforts, can also basis
These attached drawings obtain other attached drawings.
Fig. 1 is a kind of determination method flow schematic diagram of gas fired-boiler efficiency provided by one embodiment of the present invention；
Fig. 2 is a kind of determination apparatus structure block diagram of gas fired-boiler efficiency provided by one embodiment of the present invention；
Fig. 3 is the determination apparatus structure block diagram of another gas fired-boiler efficiency provided by one embodiment of the present invention；
Fig. 4 is a kind of electronic equipment of the determination method provided by one embodiment of the present invention for executing gas fired-boiler efficiency.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments, based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
As shown in Figure 1, the embodiment of the invention provides a kind of determination method of gas fired-boiler efficiency, this method may include
Following steps:
S1: the operating parameter of boiler when obtaining different load；
S2: the input and output parameter of artificial neural network to be trained is determined；
S3: the operating parameter of acquisition is normalized；
S4: randomly selecting the operating parameter of preset quantity as training sample from the corresponding operating parameter of each load,
Remaining operating parameter is as test sample；
S5: trained artificial neural network is treated using training sample and is trained to obtain simulation and prediction model；
S6: bringing test sample into simulation and prediction model and obtain emulation testing value, and judges emulation testing value and practical survey
Whether the difference of examination value is in preset allowed band, if so, determining that simulation and prediction model is gas fired-boiler efficiency Model, otherwise
Execute step S4.
Boiler operating parameter when step S1 acquires different load, that is, related operation data (e.g., the gas consumption of boiler
Rate, unit load, total air, First air air quantity, Secondary Air air quantity, furnace outlet oxygen concentration, burner hearth concentration and boiler effect
Rate etc.).For example, acquired under 40% load, 80% load and 100% load condition respectively, each 10 groups of boiler operatiopn key data,
Totally 30 groups, and in this, as the data of Artificial Neural Network Modeling.
Step S3 is in order to ensure artificial neural network will not disperse because of data and magnitude differences great disparity and cause not
Convergence, and can correctly reflect influence of the input variable to output variable.Input data, affecting parameters and output data are carried out
Normalization is standardized, input data, affecting parameters and output data are normalized in [- 1,1] section.
Step S4 is grouped boiler operatiopn data, is trained and detects to artificial neural network.For example, choosing each
As training sample, remaining 2 groups under each load are used as test sample for 8 groups of data in load.
It is engineering allowed band that preset allowed band is practical in step S6, if within the scope of engineering allowable error,
To gas fired-boiler efficiency Model；If needing to re-start network training and emulation not within the scope of engineering allowable error.
The embodiment is not only restricted to field test condition, peopleware, model relevance grade etc., by Test Data Collecting and
Artificial neural network establishes boiler combustion efficiency model, to provide the foundation for the optimization of gas fired-boiler efficiency of combustion.
In an embodiment of the invention, the detailed process of step S2 includes:
Using gas consumption rate and unit load as the input parameter of artificial neural network, with total air, First air wind
Amount, Secondary Air air quantity, furnace outlet oxygen concentration are joined as affecting parameters using boiler efficiency as the output of artificial neural network
Number.
In an embodiment of the invention, this method further include:
N1: according to the relationship between boiler controllable operating parameter and combustion characteristics, artificial neural network is determined as 3 layers；
N2: above-mentioned 3 layers of artificial neural network is optimized using genetic algorithm and obtains artificial neural network to be trained.
In this embodiment, according to the pass between Optimum Experiment data analysis boiler controllable operating parameter and combustion characteristics
System selects 3 layers of BP neural network building combustion process simulation model, and convergence error is chosen for 1 × 108；Using genetic algorithm
BP neural network is optimized, population selection 50, genetic algebra selection 100, by being encoded to network weight and threshold value,
Decoding, initialization of population and fitness calculate, and realize genetic algorithm optimizing, and then obtain better network initial value and threshold value.
As shown in Fig. 2, the embodiment of the invention provides a kind of determining device of gas fired-boiler efficiency, which includes: number
According to obtaining module, parameter determination module, normalization module, sample determining module, network training module and model judgment module,
Wherein,
The data acquisition module, the operating parameter of boiler when for obtaining different load；
The parameter determination module, for determining the input and output parameter of artificial neural network to be trained；
The normalization module, for the operating parameter of acquisition to be normalized；
The sample determining module, the operation for randomly selecting preset quantity from the corresponding operating parameter of each load are joined
Number is used as training sample, remaining operating parameter is as test sample；
The network training module is trained for treating trained artificial neural network using training sample and is emulated
Prediction model；
The model judgment module obtains emulation testing value for bringing test sample into simulation and prediction model, and judges
Whether the difference of emulation testing value and actual test value is in preset allowed band, if so, determining that simulation and prediction model is combustion
Otherwise gas boiler efficiency Model triggers the sample determining module.
Boiler operating parameter when data acquisition module acquires different load, that is, the related operation data of boiler (e.g., are fired
Gas consumption rate, unit load, total air, First air air quantity, Secondary Air air quantity, furnace outlet oxygen concentration, burner hearth concentration and
Boiler efficiency etc.).For example, being acquired under 40% load, 80% load and 100% load condition respectively, boiler operatiopn key data
Each 10 groups, totally 30 groups, and in this, as the data of Artificial Neural Network Modeling.
Normalization module be in order to ensure artificial neural network will not because of data disperse and magnitude differences great disparity and
Cause not restrain, and can correctly reflect influence of the input variable to output variable.To input data, affecting parameters and output number
According to normalization is standardized, input data, affecting parameters and output data are normalized in [- 1,1] section.
Sample determining module is grouped boiler operatiopn data, is trained and detects to artificial neural network.For example,
8 groups of data in each load are chosen as training sample, remaining 2 groups under each load are used as test sample.
It is engineering allowed band that preset allowed band is practical in model judgment module, if in engineering allowable error range
It is interior, then obtain gas fired-boiler efficiency Model；If needing to re-start network training not within the scope of engineering allowable error and imitating
Very.
The embodiment is not only restricted to field test condition, peopleware, model relevance grade etc., by Test Data Collecting and
Artificial neural network establishes boiler combustion efficiency model, to provide the foundation for the optimization of gas fired-boiler efficiency of combustion.
In an embodiment of the invention, the parameter determination module is specifically used for determining negative with gas consumption rate and unit
Input parameter of the lotus as artificial neural network, it is dense with total air, First air air quantity, Secondary Air air quantity, furnace outlet oxygen
Degree and burner hearth concentration are as affecting parameters, using boiler efficiency as the output parameter of artificial neural network.
As shown in figure 3, in an embodiment of the invention, the device further include: network design module, for according to boiler
Artificial neural network is determined as 3 layers by the relationship between controllable operating parameter and combustion characteristics, and using genetic algorithm to upper
It states 3 layers of artificial neural network and optimizes and obtain artificial neural network to be trained.
In this embodiment, according to the pass between Optimum Experiment data analysis boiler controllable operating parameter and combustion characteristics
System selects 3 layers of BP neural network building combustion process simulation model, and convergence error is chosen for 1 × 108；Using genetic algorithm
BP neural network is optimized, population selection 50, genetic algebra selection 100, by being encoded to network weight and threshold value,
Decoding, initialization of population and fitness calculate, and realize genetic algorithm optimizing, and then obtain better network initial value and threshold value.
In an embodiment of the invention, the network design module carries out the weight and threshold value of artificial neural network excellent
Change.
The contents such as each module in above-mentioned apparatus and the information exchange between unit, implementation procedure, due to side of the present invention
Method embodiment is based on same design, and for details, please refer to the description in the embodiment of the method for the present invention, and details are not described herein again.
As shown in figure 4, An embodiment provides a kind of electronic equipment.In hardware view, the electronic equipment
Including processor, optionally further comprising internal bus, network interface, memory.Wherein, memory may include memory, such as
High-speed random access memory (Random-Access Memory, RAM), it is also possible to further include nonvolatile memory (non-
Volatile memory), for example, at least 1 magnetic disk storage etc..Certainly, which is also possible that other business institutes
The hardware needed.
Processor, network interface and memory can be connected with each other by internal bus, which can be ISA
(Industry Standard Architecture, industry standard architecture) bus, PCI (Peripheral
Component Interconnect, Peripheral Component Interconnect standard) bus or EISA (Extended Industry Standard
Architecture, expanding the industrial standard structure) bus etc..The bus can be divided into address bus, data/address bus, control always
Line etc..Only to be indicated with a four-headed arrow in Fig. 4, it is not intended that an only bus or a type of convenient for indicating
Bus.
Memory, for storing program.Specifically, program may include program code, and said program code includes calculating
Machine operational order.Memory may include memory and nonvolatile memory, and provide instruction and data to processor.In one kind
In mode in the cards, processor from read in nonvolatile memory corresponding computer program into memory then transport
Row, can also obtain corresponding computer program, from other equipment with the determining device of the gas fired-boiler efficiency on logic level.
Processor executes the program that memory is stored, to realize the combustion provided in any embodiment of the present invention by the program executed
The determination method of the steam pot efficiency of furnace.
The method that the above-mentioned determining device such as Fig. 2 of the present invention or the gas fired-boiler efficiency of 3 illustrated embodiments offer executes can
To be applied in processor, or realized by processor.Processor may be a kind of IC chip, the processing with signal
Ability.During realization, each step of the above method can be by the integrated logic circuit of the hardware in processor or soft
The instruction of part form is completed.Above-mentioned processor can be general processor, including central processing unit (Central
Processing Unit, CPU), network processing unit (Network Processor, NP) etc.；It can also be Digital Signal Processing
Device (Digital Signal Processor, DSP), specific integrated circuit (Application Specific Integrated
Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other can
Programmed logic device, discrete gate or transistor logic, discrete hardware components.It may be implemented or execute present invention implementation
Disclosed each method, step and logic diagram in example.General processor can be microprocessor or the processor can also be with
It is any conventional processor etc..
The step of method in conjunction with disclosed in the embodiment of the present invention, can be embodied directly in hardware decoding processor and execute
At, or in decoding processor hardware and software module combination execute completion.Software module can be located at random access memory,
This fields such as flash memory, read-only memory, programmable read only memory or electrically erasable programmable memory, register maturation
In storage medium.The storage medium is located at memory, and processor reads the information in memory, completes above-mentioned side in conjunction with its hardware
The step of method.
The embodiment of the present invention also proposed a kind of computer readable storage medium, the computer-readable recording medium storage one
A or multiple programs, the one or more program include instruction, which holds when by the electronic equipment including multiple application programs
When row, the electronic equipment can be made to execute the determining device of the gas fired-boiler efficiency provided in any embodiment of the present invention.
Device, module or the unit that above-described embodiment illustrates can specifically be realized, Huo Zheyou by computer chip or entity
Product with certain function is realized.It is a kind of typically to realize that equipment is computer.Specifically, computer for example can be a
People's computer, laptop computer, cellular phone, camera phone, smart phone, personal digital assistant, media player, navigation
Any equipment in equipment, electronic mail equipment, game console, tablet computer, wearable device or these equipment
Combination.
For convenience of description, it describes to be divided into various units when apparatus above with function or module describes respectively.Certainly, exist
Implement to realize the function of each unit or module in the same or multiple software and or hardware when the present invention.
It should be understood by those skilled in the art that, the embodiment of the present invention can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the present invention
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the present invention, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The present invention be referring to according to the method for the embodiment of the present invention, the process of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
In a typical configuration, calculating equipment includes one or more processors (CPU), input/output interface, net
Network interface and memory.
Memory may include the non-volatile memory in computer-readable medium, random access memory (RAM) and/or
The forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flashRAM).Memory is showing for computer-readable medium
Example.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method
Or technology come realize information store.Information can be computer readable instructions, data structure, the module of program or other data.
The example of the storage medium of computer includes, but are not limited to phase change memory (PRAM), static random access memory (SRAM), moves
State random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electric erasable
Programmable read only memory (EEPROM), flash memory or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM),
Digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape magnetic disk storage or other magnetic storage devices
Or any other non-transmission medium, can be used for storage can be accessed by a computing device information.As defined in this article, it calculates
Machine readable medium does not include temporary computer readable media (transitorymedia), such as the data-signal and carrier wave of modulation.
It should be noted that, in this document, such as first and second etc relational terms are used merely to an entity
Or operation is distinguished with another entity or operation, is existed without necessarily requiring or implying between these entities or operation
Any actual relationship or order.Moreover, the terms "include", "comprise" or its any other variant be intended to it is non-
It is exclusive to include, so that the process, method, article or equipment for including a series of elements not only includes those elements,
It but also including other elements that are not explicitly listed, or further include solid by this process, method, article or equipment
Some elements.In the absence of more restrictions, the element limited by sentence " including one ", is not arranged
Except there is also other identical factors in the process, method, article or apparatus that includes the element.
Those of ordinary skill in the art will appreciate that: realize that all or part of the steps of above method embodiment can pass through
The relevant hardware of program instruction is completed, and program above-mentioned can store in computer-readable storage medium, the program
When being executed, step including the steps of the foregoing method embodiments is executed；And storage medium above-mentioned includes: ROM, RAM, magnetic disk or light
In the various media that can store program code such as disk.
Finally, it should be noted that the foregoing is merely presently preferred embodiments of the present invention, it is merely to illustrate skill of the invention
Art scheme, is not intended to limit the scope of the present invention.Any modification for being made all within the spirits and principles of the present invention,
Equivalent replacement, improvement etc., are included within the scope of protection of the present invention.
Claims (10)
1. a kind of determination method of gas fired-boiler efficiency, which is characterized in that this method comprises:
S1: the operating parameter of boiler when obtaining different load；
S2: the input and output parameter of artificial neural network to be trained is determined；
S3: the operating parameter of acquisition is normalized；
S4: the operating parameter of preset quantity is chosen from the corresponding operating parameter of each load as training sample, remaining operation
Parameter is as test sample；
S5: the artificial neural network to be trained is trained to obtain simulation and prediction model using training sample；
S6: it brings test sample into simulation and prediction model and obtains emulation testing value, and judge emulation testing value and actual test value
Difference whether in preset allowed band, if so, determine simulation and prediction model be gas fired-boiler efficiency Model, otherwise execute
Step S4.
2. the determination method of gas fired-boiler efficiency according to claim 1, which is characterized in that the detailed process packet of step S2
It includes:
Using gas consumption rate and unit load as the input parameter of artificial neural network, with total air, First air air quantity, two
Secondary wind air quantity, furnace outlet oxygen concentration are as affecting parameters, using boiler efficiency as the output parameter of artificial neural network.
3. the determination method of gas fired-boiler efficiency according to claim 1, which is characterized in that this method further include:
N1: according to the relationship between boiler controllable operating parameter and combustion characteristics, artificial neural network is determined as 3 layers；
N2: above-mentioned 3 layers of artificial neural network is optimized using genetic algorithm and obtains artificial neural network to be trained.
4. the determination method of gas fired-boiler efficiency according to claim 3, which is characterized in that the detailed process packet of step N2
It includes: the weight and threshold value of artificial neural network is optimized.
5. a kind of determining device of gas fired-boiler efficiency, which is characterized in that the device includes: data acquisition module, parameter determination
Module, normalization module, sample determining module, network training module and model judgment module, wherein
The data acquisition module, the operating parameter of boiler when for obtaining different load；
The parameter determination module, for determining the input and output parameter of artificial neural network to be trained；
The normalization module, for the operating parameter of acquisition to be normalized；
The sample determining module, for choosing the operating parameter of preset quantity from the corresponding operating parameter of each load as instruction
Practice sample, remaining operating parameter is as test sample；
The network training module is emulated for being trained using training sample to the artificial neural network to be trained
Prediction model；
The model judgment module obtains emulation testing value for bringing test sample into simulation and prediction model, and judges to emulate
Whether the difference of test value and actual test value is in preset allowed band, if so, determining that simulation and prediction model is gas-fired boiler
Otherwise efficiency of furnace model triggers the sample determining module.
6. the determining device of gas fired-boiler efficiency according to claim 5, which is characterized in that the parameter determination module is specific
For determining using gas consumption rate and unit load as the input parameter of artificial neural network, with total air, First air wind
Amount, Secondary Air air quantity, furnace outlet oxygen concentration are joined as affecting parameters using boiler efficiency as the output of artificial neural network
Number.
7. the determining device of gas fired-boiler efficiency according to claim 5, which is characterized in that described device further include: network
Module is designed,
For according to the relationship between boiler controllable operating parameter and combustion characteristics, artificial neural network is determined as 3 layers, and
Above-mentioned 3 layers of artificial neural network is optimized using genetic algorithm and obtains artificial neural network to be trained.
8. the determining device of gas fired-boiler efficiency according to claim 7, which is characterized in that the network design module is to people
The weight and threshold value of artificial neural networks optimize.
9. a kind of readable medium, which is characterized in that the readable medium includes executing instruction, when the processor of electronic equipment executes institute
It states when executing instruction, the electronic equipment executes the determination method of the gas fired-boiler efficiency as described in any in Claims 1-4.
10. a kind of electronic equipment, which is characterized in that the electronic equipment includes: processor, memory and bus；The memory
It being executed instruction for storing, the processor is connect with the memory by the bus, when electronic equipment operation,
The processor executes the described of memory storage and executes instruction, so that the processor is executed as in Claims 1-4
The determination method of any gas fired-boiler efficiency.
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| CN201910075978.7A CN109858136A (en) | 2019-01-26 | 2019-01-26 | A kind of determination method and apparatus of gas fired-boiler efficiency |
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| CN201910075978.7A CN109858136A (en) | 2019-01-26 | 2019-01-26 | A kind of determination method and apparatus of gas fired-boiler efficiency |
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Cited By (14)
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| CN110288161A (en) * | 2019-06-28 | 2019-09-27 | 新奥数能科技有限公司 | The adjusting method and device of the blow valve of gas fired-boiler |
| CN110285444A (en) * | 2019-06-28 | 2019-09-27 | 新奥数能科技有限公司 | The regulation method and device of gas fired-boiler |
| CN110673485A (en) * | 2019-10-21 | 2020-01-10 | 京东城市(南京)科技有限公司 | Model training method, device, electronic apparatus, and medium for combustion control |
| CN110705881A (en) * | 2019-10-08 | 2020-01-17 | 武汉市政工程设计研究院有限责任公司 | Boiler efficiency online calculation method and system based on artificial neural network |
| CN110794688A (en) * | 2020-01-06 | 2020-02-14 | 汉谷云智(武汉)科技有限公司 | Intelligent operation optimization method and system for gas boiler unit and storage medium |
| CN111339642A (en) * | 2020-02-13 | 2020-06-26 | 创新奇智(合肥)科技有限公司 | Simulation model calibration method, system, readable medium and device |
| CN111639793A (en) * | 2020-05-13 | 2020-09-08 | 新奥数能科技有限公司 | Boiler group scheduling optimization method and device |
| CN111695249A (en) * | 2020-05-29 | 2020-09-22 | 广东省特种设备检测研究院顺德检测院 | Prediction method for heat efficiency of gas-fired boiler |
| CN112182744A (en) * | 2020-09-10 | 2021-01-05 | 东风汽车集团有限公司 | EGR (exhaust gas Recirculation) rate prediction method, device, equipment and medium |
| CN112580740A (en) * | 2020-12-28 | 2021-03-30 | 北方工业大学 | Ozone concentration measuring method, device, electronic device and storage medium |
| CN113312787A (en) * | 2021-06-11 | 2021-08-27 | 北京宇衡金凯技术服务有限公司 | Circulating fluidized bed boiler simulation method and system and computer storage medium |
| CN113553765A (en) * | 2021-07-14 | 2021-10-26 | 煤科院节能技术有限公司 | Dynamic simulation method, device and system for boiler operation process |
| CN113591283A (en) * | 2021-07-15 | 2021-11-02 | 新奥数能科技有限公司 | Method and device for adjusting running oxygen amount of gas-fired boiler and computer equipment |
| WO2024040608A1 (en) * | 2022-08-26 | 2024-02-29 | 西门子股份公司 | Model training method for energy management system, apparatus, and storage medium |
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| CN110285444A (en) * | 2019-06-28 | 2019-09-27 | 新奥数能科技有限公司 | The regulation method and device of gas fired-boiler |
| CN110288161A (en) * | 2019-06-28 | 2019-09-27 | 新奥数能科技有限公司 | The adjusting method and device of the blow valve of gas fired-boiler |
| CN110705881A (en) * | 2019-10-08 | 2020-01-17 | 武汉市政工程设计研究院有限责任公司 | Boiler efficiency online calculation method and system based on artificial neural network |
| CN110673485A (en) * | 2019-10-21 | 2020-01-10 | 京东城市(南京)科技有限公司 | Model training method, device, electronic apparatus, and medium for combustion control |
| CN110673485B (en) * | 2019-10-21 | 2020-11-24 | 京东城市(南京)科技有限公司 | Model training method, device, electronic apparatus, and medium for combustion control |
| CN110794688A (en) * | 2020-01-06 | 2020-02-14 | 汉谷云智(武汉)科技有限公司 | Intelligent operation optimization method and system for gas boiler unit and storage medium |
| CN110794688B (en) * | 2020-01-06 | 2020-05-05 | 汉谷云智(武汉)科技有限公司 | Intelligent operation optimization method and system for gas boiler unit and storage medium |
| CN111339642B (en) * | 2020-02-13 | 2023-09-15 | 创新奇智(合肥)科技有限公司 | Simulation model calibrating method, system, readable medium and equipment |
| CN111339642A (en) * | 2020-02-13 | 2020-06-26 | 创新奇智(合肥)科技有限公司 | Simulation model calibration method, system, readable medium and device |
| CN111639793A (en) * | 2020-05-13 | 2020-09-08 | 新奥数能科技有限公司 | Boiler group scheduling optimization method and device |
| CN111695249A (en) * | 2020-05-29 | 2020-09-22 | 广东省特种设备检测研究院顺德检测院 | Prediction method for heat efficiency of gas-fired boiler |
| CN112182744A (en) * | 2020-09-10 | 2021-01-05 | 东风汽车集团有限公司 | EGR (exhaust gas Recirculation) rate prediction method, device, equipment and medium |
| CN112182744B (en) * | 2020-09-10 | 2022-11-04 | 东风汽车集团有限公司 | EGR rate prediction method, device, equipment and medium |
| CN112580740A (en) * | 2020-12-28 | 2021-03-30 | 北方工业大学 | Ozone concentration measuring method, device, electronic device and storage medium |
| CN112580740B (en) * | 2020-12-28 | 2024-03-01 | 北方工业大学 | Ozone concentration measuring method, ozone concentration measuring device, electronic equipment and storage medium |
| CN113312787A (en) * | 2021-06-11 | 2021-08-27 | 北京宇衡金凯技术服务有限公司 | Circulating fluidized bed boiler simulation method and system and computer storage medium |
| CN113553765A (en) * | 2021-07-14 | 2021-10-26 | 煤科院节能技术有限公司 | Dynamic simulation method, device and system for boiler operation process |
| CN113553765B (en) * | 2021-07-14 | 2024-02-20 | 北京天地融创科技股份有限公司 | Dynamic simulation method, device and system for boiler operation process |
| CN113591283A (en) * | 2021-07-15 | 2021-11-02 | 新奥数能科技有限公司 | Method and device for adjusting running oxygen amount of gas-fired boiler and computer equipment |
| CN113591283B (en) * | 2021-07-15 | 2023-10-10 | 新奥数能科技有限公司 | Method and device for adjusting operating oxygen amount of gas boiler and computer equipment |
| WO2024040608A1 (en) * | 2022-08-26 | 2024-02-29 | 西门子股份公司 | Model training method for energy management system, apparatus, and storage medium |
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