CN112856478A - Method, device, equipment and medium for adjusting air-fuel ratio of gas boiler - Google Patents
Method, device, equipment and medium for adjusting air-fuel ratio of gas boiler Download PDFInfo
- Publication number
- CN112856478A CN112856478A CN202110046375.1A CN202110046375A CN112856478A CN 112856478 A CN112856478 A CN 112856478A CN 202110046375 A CN202110046375 A CN 202110046375A CN 112856478 A CN112856478 A CN 112856478A
- Authority
- CN
- China
- Prior art keywords
- data
- oxygen content
- gas boiler
- historical
- gas
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23N—REGULATING OR CONTROLLING COMBUSTION
- F23N1/00—Regulating fuel supply
- F23N1/005—Regulating fuel supply using electrical or electromechanical means
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Regulation And Control Of Combustion (AREA)
Abstract
The disclosed embodiments of the invention disclose methods, apparatus, devices and media for air-fuel ratio adjustment of a gas boiler. The method comprises the following steps: acquiring a training sample set, wherein the training sample set comprises historical data samples of a gas boiler and historical smoke and oxygen content data samples of the gas boiler corresponding to the data samples of the gas boiler; training a model to be trained according to the training sample set to obtain a prediction model; acquiring real-time data of the gas boiler; inputting the real-time data into the prediction model to obtain the data of the oxygen content in the smoke discharged by the gas-fired boiler; and adjusting the air-fuel ratio of the gas-fired boiler according to the smoke and oxygen content data. This embodiment has realized more intellectuality to gas boiler, and simpler maintenance promotes work efficiency.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to an air-fuel ratio adjusting method, device, equipment and medium of a gas boiler.
Background
At present, a zirconia oxygen sensor is generally used in industry to measure the oxygen content of flue gas, and the sensor has the defects of high cost, large measurement lag, difficult maintenance, large measurement error, short service life and the like, and is not suitable for being used in a scene that distributed energy sources mainly comprise small and medium-sized gas boilers.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary of the disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The disclosed embodiments of the present invention provide a method, apparatus, device and medium for adjusting the air-fuel ratio of a gas boiler to solve the technical problems mentioned in the background section above.
In a first aspect, a disclosed embodiment of the present invention provides a method of adjusting an air-fuel ratio of a gas boiler, the method including: acquiring a training sample set, wherein the training sample set comprises historical data samples of a gas boiler and historical smoke and oxygen content data samples of the gas boiler corresponding to the data samples of the gas boiler; training a model to be trained according to the training sample set to obtain a prediction model; acquiring real-time data of the gas boiler; inputting the real-time data into the prediction model to obtain the data of the oxygen content in the smoke discharged by the gas-fired boiler; and adjusting the air-fuel ratio of the gas-fired boiler according to the smoke and oxygen content data.
In a second aspect, a disclosed embodiment of the present invention provides an air-fuel ratio adjusting apparatus of a gas boiler, the apparatus including: a first obtaining unit configured to obtain a training sample set, wherein the training sample set comprises historical data samples of a gas boiler and historical smoke and oxygen content data samples of the gas boiler corresponding to the data samples of the gas boiler; the training unit is configured to train a model to be trained according to the training sample set to obtain a prediction model; a second acquisition unit configured to acquire real-time data of the gas boiler; a determining unit configured to input the real-time data into the prediction model to obtain the data of the oxygen content of the flue gas of the gas boiler; an adjusting unit configured to adjust an air-fuel ratio of the gas boiler according to the flue gas oxygen content data.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement the method as described in the first aspect.
In a fourth aspect, embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method as described in the first aspect.
One of the above embodiments disclosed by the invention has the following beneficial effects: firstly, a training sample set is obtained, and then a model to be trained is trained according to the training sample set to obtain a prediction model. Because the historical data samples in the training sample set are matched with the historical smoke and oxygen content data samples in the training sample set, the prediction model obtained through training has higher accuracy. And then acquiring real-time data of the gas-fired boiler, and inputting the real-time data into the prediction model to obtain the oxygen content of the discharged smoke of the gas-fired boiler. And finally, adjusting the air-fuel ratio of the gas boiler according to the data of the oxygen content of the discharged smoke. The mode of determining the oxygen content of the discharged smoke of the gas boiler by using the model enables the obtained data to be more accurate, and reduces operation and maintenance cost, so that the invention realizes more intellectualization and simpler maintenance of the gas boiler, and improves the working efficiency. The invention discloses a method for predicting the oxygen content of flue gas of a gas-fired boiler based on machine learning, which needs a small amount of label data of the oxygen content of the flue gas of the gas-fired boiler, predicts the oxygen content of the discharged flue gas of the gas-fired boiler by a machine learning model by utilizing other existing easily-obtained data, and can overcome the defect of measurement by using an actual sensor. In addition, the air-fuel ratio of the gas boiler is adjusted, so that fuel can be fully combusted, energy is saved, and the energy utilization rate is improved.
Drawings
The above and other features, advantages and aspects of the disclosed embodiments will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a schematic diagram of an application scenario of a method of adjusting an air-fuel ratio of a gas boiler in accordance with a disclosed embodiment of the invention;
FIG. 2 is a flow chart of an embodiment of a method of air-fuel ratio adjustment of a gas boiler according to the present disclosure;
FIG. 3 is a schematic structural view of an embodiment of an air-fuel ratio adjusting apparatus of a gas boiler according to the present disclosure;
FIG. 4 is a schematic block diagram of an electronic device suitable for use in implementing the disclosed embodiments of the invention.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments disclosed in the present invention may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules, or units, and are not used for limiting the order or interdependence of the functions performed by the devices, modules, or units.
It is noted that references to "a", "an", and "the" modifications in the disclosure are exemplary rather than limiting, and that those skilled in the art will understand that "one or more" unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the disclosed embodiments are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
FIG. 1 is a schematic diagram of an application scenario of a method of air-fuel ratio adjustment for a gas boiler according to some embodiments of the present disclosure.
In the application scenario of fig. 1, first, the computing device 101 may obtain a training sample set 102. Then, the computing device 101 may train the model 103 to be trained according to the training sample set 102, so as to obtain a prediction model 104. Thereafter, the computing device 101 may obtain real-time data 105 of the gas boiler. Then, the computing device 101 may input the real-time data 105 into the predictive model 104 to obtain the flue gas oxygen content data 106 of the gas boiler. Finally, the computing device 101 may adjust the air-fuel ratio 107 of the gas boiler based on the flue gas oxygen content data 106.
The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as implementation needs dictate.
With continued reference to FIG. 2, a flowchart 200 of an embodiment of a method of adjusting an air-fuel ratio of a gas boiler in accordance with the present disclosure is shown. The method may be performed by the computing device 101 of fig. 1. The air-fuel ratio adjusting method of the gas boiler comprises the following steps:
In an embodiment, an executing body (such as the computing device 101 shown in fig. 1) of the air-fuel ratio adjusting method of the gas boiler may obtain the training sample set through a wired connection manner or a wireless connection manner. Here, the training sample set includes historical data samples of a gas boiler, and historical carbon monoxide emission samples of the gas boiler corresponding to the data samples of the gas boiler. As an example, the above history data sample may be a gas flow rate, a gas temperature, a gas pressure, an air temperature, an air flow rate, a feed water temperature, a main steam pressure, a main steam temperature, a main steam flow rate, and the like of the gas boiler history.
It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
In an alternative implementation of the embodiment, the obtaining of the training sample set includes the following steps: the method comprises the following steps that firstly, the execution main body can acquire historical data information of the gas boiler and historical smoke and oxygen content data information corresponding to the historical data information; secondly, the execution main body can perform data cleaning on the historical data information and the historical smoke exhaust and oxygen content data information to obtain cleaned data; thirdly, the execution main body can process the cleaned data to obtain target data; fourthly, the executing body can determine the historical data information in the target data as a historical data sample, and determine the historical smoke exhaust and oxygen content data information in the target data as a historical smoke exhaust and oxygen content data sample to obtain a training sample set.
In an optional implementation manner of the embodiment, the performing data cleaning on the historical data information and the historical smoke exhaust oxygen content data information to obtain cleaned data includes:
and performing data cleaning on the historical data information and the historical smoke exhaust and oxygen content data information according to the following formula to obtain cleaned data:
wherein,
representing the mean value of all sampling data of the jth variable; var (A)j) Represents the variance of all sampled data for the jth variable;
the cleaned data represents the jth variable of the ith sampling point after data cleaning; i represents the ith sample point; j represents the jth variable;
data representing the jth variable at the ith sample point.
In an optional implementation manner of the embodiment, the processing the cleaned data to obtain target data includes: abnormal data elimination is carried out on the cleaned data to obtain eliminated data; and performing delay compensation on each data in the removed data to obtain compensated data so as to form target data. Here, the abnormal data generally refers to data that differs from the average data by more than a threshold value. The delay compensation is generally a way to add or subtract a delay time value from the flushed data. As an example, the execution main body may load a step change instruction to the control system of the gas flow rate, record a time from when the gas flow rate starts to change to when the steam flow rate starts to change significantly in the same direction as the delay time, and perform delay compensation on the post-purge data.
And 202, training a model to be trained according to the training sample set to obtain a prediction model.
In an embodiment, the executing entity may train the model to be trained according to the training sample set to obtain the prediction model.
As an example, the execution subject may input the historical data samples in the training sample set to a model to be trained, so as to obtain smoke exhaust and oxygen content data; comparing the smoke exhaust oxygen content data with the historical smoke exhaust oxygen content data sample to obtain a comparison result; and in response to the fact that the comparison result does not meet the preset condition, determining that the training of the model to be trained is not finished, and adjusting related parameters in the model to be trained.
In an optional implementation manner of the embodiment, in response to determining that the comparison result satisfies the preset condition, it is determined that the model to be trained is trained, and the model to be trained is determined as a prediction model.
In an optional implementation manner of the embodiment, the model to be trained may be a neural network model adopting an XGBoost algorithm.
And step 203, acquiring real-time data of the gas boiler.
In an embodiment, the execution main body may acquire the real-time data of the gas boiler through a wired connection manner or a wireless connection manner. The real-time data generally refers to real-time gas flow, gas temperature, gas pressure, air temperature, air flow, feed water temperature, main steam pressure, main steam temperature, main steam flow, and the like of the gas boiler.
And step 204, inputting the real-time data into the prediction model to obtain the data of the oxygen content of the discharged smoke of the gas-fired boiler.
In an embodiment, the execution body may input the real-time data to the prediction model to obtain the flue gas oxygen content data of the gas boiler.
And step 205, adjusting the air-fuel ratio of the gas-fired boiler according to the smoke oxygen content data.
In an embodiment, the executing body may adjust an air-fuel ratio of the gas boiler by: the first step, the execution main body can obtain the design value of the oxygen content of the discharged smoke of the gas boiler; secondly, the execution main body can perform difference calculation on the design value of the oxygen content of the discharged smoke and the data of the oxygen content of the discharged smoke to obtain a difference calculation result; third, the execution body may generate an air-fuel ratio command based on the difference result; fourthly, the execution main body may multiply the air-fuel ratio command and the acquired gas flow rate of the gas boiler to obtain a multiplication result; a fifth step in which the execution body generates an air flow command based on the multiplication result; in the sixth step, the execution body may adjust an air flow rate into the gas boiler based on the air flow rate command.
One of the above embodiments disclosed by the invention has the following beneficial effects: firstly, a training sample set is obtained, and then a model to be trained is trained according to the training sample set to obtain a prediction model. Because the historical data samples in the training sample set are matched with the historical smoke and oxygen content data samples in the training sample set, the prediction model obtained through training has higher accuracy. And then acquiring real-time data of the gas-fired boiler, and inputting the real-time data into the prediction model to obtain the oxygen content of the discharged smoke of the gas-fired boiler. And finally, adjusting the air-fuel ratio of the gas boiler according to the data of the oxygen content of the discharged smoke. The mode of determining the oxygen content of the discharged smoke of the gas boiler by using the model enables the obtained data to be more accurate, and reduces operation and maintenance cost, so that the invention realizes more intellectualization and simpler maintenance of the gas boiler, and improves the working efficiency. The invention discloses a method for predicting the oxygen content of flue gas of a gas-fired boiler based on machine learning, which needs a small amount of label data of the oxygen content of the flue gas of the gas-fired boiler, predicts the oxygen content of the discharged flue gas of the gas-fired boiler by a machine learning model by utilizing other existing easily-obtained data, and can overcome the defect of measurement by using an actual sensor. In addition, the air-fuel ratio of the gas boiler is adjusted, so that fuel can be fully combusted, energy is saved, and the energy utilization rate is improved.
With further reference to FIG. 3, as an implementation of the above-described method for each of the above-described figures, the present disclosure provides some embodiments of an air-fuel ratio adjusting apparatus for a gas boiler, which correspond to those of the above-described method embodiments of FIG. 2, and which may be particularly applicable to various electronic devices.
As shown in fig. 3, the air-fuel ratio adjusting apparatus 300 of the gas boiler of the embodiment includes: a first acquisition unit 301, a training unit 302, a second acquisition unit 303, a determination unit 304 and an adjustment unit 305. The first obtaining unit 301 is configured to obtain a training sample set, where the training sample set includes historical data samples of a gas boiler, and historical smoke and oxygen content data samples of the gas boiler corresponding to the data samples of the gas boiler; a training unit 302 configured to train a model to be trained according to the training sample set, so as to obtain a prediction model; a second obtaining unit 303 configured to obtain real-time data of the gas boiler; a determination unit 304 configured to input the real-time data into the prediction model, resulting in smoke and oxygen content data of the gas boiler; an adjusting unit 305 configured to adjust an air-fuel ratio of the gas boiler according to the flue gas oxygen content data.
In an alternative implementation of the embodiment, the first obtaining unit 301 of the air-fuel ratio adjusting apparatus 300 of the gas boiler is further configured to: acquiring historical data information of the gas boiler and historical smoke and oxygen content data information corresponding to the historical data information; performing data cleaning on the historical data information and the historical smoke exhaust oxygen content data information to obtain cleaned data; processing the cleaned data to obtain target data; determining historical data information in the target data as a historical data sample, and determining historical smoke and oxygen content data information in the target data as a historical smoke and oxygen content data sample to obtain a training sample set.
In an optional implementation manner of the embodiment, the performing data cleaning on the historical data information and the historical smoke exhaust oxygen content data information to obtain cleaned data includes: and performing data cleaning on the historical data information and the historical smoke exhaust and oxygen content data information according to the following formula to obtain cleaned data:
wherein,
representing the mean value of all sampling data of the jth variable; var (A)j) Represents the variance of all sampled data for the jth variable;
the cleaned data represents the jth variable of the ith sampling point after data cleaning; i represents the ith sample point; j represents the jth variable;
data representing the jth variable at the ith sample point.
In an optional implementation manner of the embodiment, the processing the cleaned data to obtain target data includes: abnormal data elimination is carried out on the cleaned data to obtain eliminated data; and performing delay compensation on each data in the removed data to obtain compensated data so as to form target data.
In an alternative implementation of the embodiment, the training unit 302 of the air-fuel ratio adjusting apparatus 300 of the gas boiler is further configured to: inputting historical data samples in the training sample set into a model to be trained to obtain smoke exhaust and oxygen content data; comparing the smoke exhaust oxygen content data with the historical smoke exhaust oxygen content data sample to obtain a comparison result; and in response to the fact that the comparison result does not meet the preset condition, determining that the training of the model to be trained is not finished, and adjusting related parameters in the model to be trained.
In an alternative implementation of the embodiment, the air-fuel ratio adjusting apparatus 300 of the gas boiler is further configured to: in response to determining that the comparison result meets the preset condition, determining that the model to be trained is trained, and determining the model to be trained as a prediction model.
In an alternative implementation of the embodiment, the adjusting unit 305 of the air-fuel ratio adjusting apparatus 300 of the gas boiler is further configured to: acquiring a design value of the oxygen content of the discharged smoke of the gas boiler; performing difference calculation on the design value of the oxygen content of the exhaust smoke and the data of the oxygen content of the exhaust smoke to obtain a difference calculation result; generating an air-fuel ratio command based on the difference result; multiplying the air-fuel ratio instruction and the acquired gas flow of the gas-fired boiler to obtain a multiplication result; generating an air flow command based on the multiplication result; adjusting an air flow into the gas boiler based on the air flow command.
It will be understood that the units described in the apparatus 300 correspond to the various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 300 and the units included therein, and are not described herein again.
Referring now to FIG. 4, a block diagram of an electronic device (e.g., computing device 101 of FIG. 1)400 suitable for use in implementing some embodiments of the present disclosure is shown. The server illustrated in fig. 4 is only an example and should not bring any limitations to the function and scope of use of the disclosed embodiments of the present invention.
As shown in fig. 4, electronic device 400 may include a processing device (e.g., central processing unit, graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage device 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate wirelessly or by wire with other devices to exchange data. While fig. 4 illustrates an electronic device 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 4 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through the communication device 409, or from the storage device 408, or from the ROM 402. Which when executed by the processing means 401, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium mentioned above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a training sample set, wherein the training sample set comprises historical data samples of a gas boiler and historical smoke and oxygen content data samples of the gas boiler corresponding to the data samples of the gas boiler; training a model to be trained according to the training sample set to obtain a prediction model; acquiring real-time data of the gas boiler; inputting the real-time data into the prediction model to obtain the data of the oxygen content in the smoke discharged by the gas-fired boiler; and adjusting the air-fuel ratio of the gas-fired boiler according to the smoke and oxygen content data.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a first acquisition unit, a training unit, a second acquisition unit, a determination unit, and an adjustment unit. Where the names of these units do not in some cases constitute a limitation of the unit itself, for example, the first obtaining unit may also be described as "a unit that obtains a training sample set, where the training sample set includes historical data samples of the gas boiler, and historical flue gas oxygen content data samples of the gas boiler corresponding to the data samples of the gas boiler".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the present disclosure and is provided for the purpose of illustrating the general principles of the technology. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments disclosed in the present application is not limited to the embodiments with specific combinations of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. A method of adjusting an air-fuel ratio of a gas boiler, comprising:
acquiring a training sample set, wherein the training sample set comprises historical data samples of a gas boiler and historical smoke and oxygen content data samples of the gas boiler corresponding to the data samples of the gas boiler;
training a model to be trained according to the training sample set to obtain a prediction model;
acquiring real-time data of the gas boiler;
inputting the real-time data into the prediction model to obtain the data of the oxygen content in the smoke discharged by the gas-fired boiler;
and adjusting the air-fuel ratio of the gas-fired boiler according to the smoke and oxygen content data.
2. The method of claim 1, wherein the obtaining a training sample set comprises:
acquiring historical data information of the gas boiler and historical smoke and oxygen content data information corresponding to the historical data information;
performing data cleaning on the historical data information and the historical smoke exhaust oxygen content data information to obtain cleaned data;
processing the cleaned data to obtain target data;
determining historical data information in the target data as a historical data sample, and determining historical smoke and oxygen content data information in the target data as a historical smoke and oxygen content data sample to obtain a training sample set.
3. The method of claim 2, wherein the step of performing data cleaning on the historical data information and the historical soot oxygen content data information to obtain cleaned data comprises:
and performing data cleaning on the historical data information and the historical smoke exhaust and oxygen content data information according to the following formula to obtain cleaned data:
wherein,
representing the mean value of all sampling data of the jth variable; var (A)j) Represents the variance of all sampled data for the jth variable;
the cleaned data represents the jth variable of the ith sampling point after data cleaning; i represents the ith sample point; j represents the jth variable;
data representing the jth variable at the ith sample point.
4. The method of claim 2, wherein the processing the cleaned data to obtain target data comprises:
abnormal data elimination is carried out on the cleaned data to obtain eliminated data;
and performing delay compensation on each data in the removed data to obtain compensated data so as to form target data.
5. The method for adjusting the air-fuel ratio of a gas boiler as claimed in one of claims 1 to 4, wherein said training a model to be trained according to said training sample set to obtain a prediction model comprises:
inputting historical data samples in the training sample set into a model to be trained to obtain smoke exhaust and oxygen content data;
comparing the smoke exhaust oxygen content data with the historical smoke exhaust oxygen content data sample to obtain a comparison result;
and in response to the fact that the comparison result does not meet the preset condition, determining that the training of the model to be trained is not finished, and adjusting related parameters in the model to be trained.
6. The air-fuel ratio adjusting method of a gas boiler according to claim 5, characterized in that the method further comprises:
in response to determining that the comparison result meets the preset condition, determining that the model to be trained is trained, and determining the model to be trained as a prediction model.
7. The method of claim 6, wherein the adjusting the air-fuel ratio of the gas boiler according to the flue gas oxygen content data comprises:
acquiring a design value of the oxygen content of the discharged smoke of the gas boiler;
performing difference calculation on the design value of the oxygen content of the exhaust smoke and the data of the oxygen content of the exhaust smoke to obtain a difference calculation result;
generating an air-fuel ratio command based on the difference result;
multiplying the air-fuel ratio instruction and the acquired gas flow of the gas-fired boiler to obtain a multiplication result;
generating an air flow command based on the multiplication result;
adjusting an air flow into the gas boiler based on the air flow command.
8. An air-fuel ratio adjusting apparatus of a gas boiler, characterized by comprising:
a first obtaining unit configured to obtain a training sample set, wherein the training sample set comprises historical data samples of a gas boiler and historical smoke and oxygen content data samples of the gas boiler corresponding to the data samples of the gas boiler;
the training unit is configured to train a model to be trained according to the training sample set to obtain a prediction model;
a second acquisition unit configured to acquire real-time data of the gas boiler;
a determining unit configured to input the real-time data into the prediction model to obtain the data of the oxygen content of the flue gas of the gas boiler;
an adjusting unit configured to adjust an air-fuel ratio of the gas boiler according to the flue gas oxygen content data.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110046375.1A CN112856478A (en) | 2021-01-14 | 2021-01-14 | Method, device, equipment and medium for adjusting air-fuel ratio of gas boiler |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110046375.1A CN112856478A (en) | 2021-01-14 | 2021-01-14 | Method, device, equipment and medium for adjusting air-fuel ratio of gas boiler |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112856478A true CN112856478A (en) | 2021-05-28 |
Family
ID=76003694
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110046375.1A Pending CN112856478A (en) | 2021-01-14 | 2021-01-14 | Method, device, equipment and medium for adjusting air-fuel ratio of gas boiler |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112856478A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114118540A (en) * | 2021-11-11 | 2022-03-01 | 新智我来网络科技有限公司 | Method and device for predicting oxygen content in flue gas based on sample migration |
| CN114187976A (en) * | 2021-11-26 | 2022-03-15 | 国家能源蓬莱发电有限公司 | Oxygen amount prediction method based on real-time measurement data |
| CN115451424A (en) * | 2022-08-12 | 2022-12-09 | 北京全应科技有限公司 | Coal-fired boiler coal feeding control method based on pressure feedforward |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201507945U (en) * | 2009-09-28 | 2010-06-16 | 上海工业锅炉研究所 | Fuel gas boiler control adjustment and remote monitoring system |
| CN108595757A (en) * | 2018-03-21 | 2018-09-28 | 新智数字科技有限公司 | Energy device modeling, energy source station dispatching method, device and equipment |
| KR20200061931A (en) * | 2018-11-26 | 2020-06-03 | 두산중공업 주식회사 | Apparatus for generating learning data for combustion optimization and method thereof |
| CN111396846A (en) * | 2020-03-24 | 2020-07-10 | 山信软件股份有限公司 | Intelligent control method for boiler |
| CN111623369A (en) * | 2020-06-28 | 2020-09-04 | 华电潍坊发电有限公司 | Control method for adjusting boiler fuel feeding quantity by using smoke oxygen content signal |
| CN111651847A (en) * | 2019-02-18 | 2020-09-11 | 新奥数能科技有限公司 | Method and device for determining running oxygen amount of gas-fired boiler |
| CN112178684A (en) * | 2020-09-30 | 2021-01-05 | 湖北中烟工业有限责任公司 | A system and method for improving boiler air/fuel ratio accuracy |
-
2021
- 2021-01-14 CN CN202110046375.1A patent/CN112856478A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201507945U (en) * | 2009-09-28 | 2010-06-16 | 上海工业锅炉研究所 | Fuel gas boiler control adjustment and remote monitoring system |
| CN108595757A (en) * | 2018-03-21 | 2018-09-28 | 新智数字科技有限公司 | Energy device modeling, energy source station dispatching method, device and equipment |
| KR20200061931A (en) * | 2018-11-26 | 2020-06-03 | 두산중공업 주식회사 | Apparatus for generating learning data for combustion optimization and method thereof |
| CN111651847A (en) * | 2019-02-18 | 2020-09-11 | 新奥数能科技有限公司 | Method and device for determining running oxygen amount of gas-fired boiler |
| CN111396846A (en) * | 2020-03-24 | 2020-07-10 | 山信软件股份有限公司 | Intelligent control method for boiler |
| CN111623369A (en) * | 2020-06-28 | 2020-09-04 | 华电潍坊发电有限公司 | Control method for adjusting boiler fuel feeding quantity by using smoke oxygen content signal |
| CN112178684A (en) * | 2020-09-30 | 2021-01-05 | 湖北中烟工业有限责任公司 | A system and method for improving boiler air/fuel ratio accuracy |
Non-Patent Citations (3)
| Title |
|---|
| 厉彦忠: "《热能与动力机械测试技术》", 31 July 2020, 西安交通大学出版社, pages: 17 - 19 * |
| 李希胜等: "《自动检测技术》", 冶金工业出版社, pages: 304 - 305 * |
| 赵钢等: "燃气锅炉燃烧效率优化控制仿真研究", 《计算机仿真》, vol. 34, no. 01, 15 January 2017 (2017-01-15), pages 376 - 379 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114118540A (en) * | 2021-11-11 | 2022-03-01 | 新智我来网络科技有限公司 | Method and device for predicting oxygen content in flue gas based on sample migration |
| CN114187976A (en) * | 2021-11-26 | 2022-03-15 | 国家能源蓬莱发电有限公司 | Oxygen amount prediction method based on real-time measurement data |
| CN114187976B (en) * | 2021-11-26 | 2024-07-12 | 国家能源蓬莱发电有限公司 | A method for predicting oxygen content based on real-time measurement data |
| CN115451424A (en) * | 2022-08-12 | 2022-12-09 | 北京全应科技有限公司 | Coal-fired boiler coal feeding control method based on pressure feedforward |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2022077946A1 (en) | Data measurement method and apparatus, and electronic device and computer-readable medium | |
| CN108880931B (en) | Method and apparatus for outputting information | |
| US20190362490A1 (en) | Method and apparatus for inspecting corrosion defect of ladle | |
| CN112856478A (en) | Method, device, equipment and medium for adjusting air-fuel ratio of gas boiler | |
| CN118583038B (en) | Terminal detects frock | |
| CN112991695A (en) | Energy efficiency abnormity early warning method and device for gas-fired boiler, electronic equipment and medium | |
| US10571446B2 (en) | Data quality control using a correlated sensor group | |
| CN111683156A (en) | Information pushing method and device, electronic equipment and computer readable medium | |
| CN113361701A (en) | Quantification method and device of neural network model | |
| CN112182067A (en) | Individual height prediction method and device, electronic equipment and storage medium | |
| CN110084298B (en) | Method and device for detecting image similarity | |
| CN114582433A (en) | Method and device for determining oxygen content of flue gas | |
| CN117787216A (en) | Training method and device of format conversion model, electronic equipment and storage medium | |
| CN114764539A (en) | Method, device, equipment and medium for determining oxygen content of discharged smoke of gas turbine | |
| CN114765065A (en) | Method, device, equipment and medium for determining oxygen content of flue gas of gas internal combustion engine | |
| WO2022216599A1 (en) | System for generating natural language comment texts for multi-variate time series | |
| CN117236805B (en) | Power equipment control methods, devices, electronic equipment and computer-readable media | |
| CN114764540A (en) | Method, device, equipment and medium for determining carbon monoxide emission of gas turbine | |
| CN118410375A (en) | A deep integration approach between information technology and operational technology | |
| CN115983121A (en) | Method and device for refinement of coastal NWP data grid based on double mapping | |
| CN114764584A (en) | Method, device and equipment for determining carbon monoxide emission of gas internal combustion engine | |
| CN114595840A (en) | Method, apparatus, apparatus and medium for determining maintenance time of equipment components | |
| CN114638378A (en) | Method, apparatus, device and medium for determining maintenance time of device component | |
| CN116429968B (en) | Sample migration-based flue gas oxygen content prediction method and device and electronic equipment | |
| CN113849540A (en) | Fault prediction model training and prediction method, device, electronic equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210528 |