CN115753642A - Industrial carbon emission online detection method and device - Google Patents
Industrial carbon emission online detection method and device Download PDFInfo
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- CN115753642A CN115753642A CN202211595074.5A CN202211595074A CN115753642A CN 115753642 A CN115753642 A CN 115753642A CN 202211595074 A CN202211595074 A CN 202211595074A CN 115753642 A CN115753642 A CN 115753642A
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Abstract
The application discloses an industrial carbon emission online detection method, which comprises the following steps: monitoring industrial gas emission, and detecting the concentration of carbon element related gas; calculating the real-time carbon emission according to the gas concentration and the flow; and acquiring real-time capacity data of a plant area, and comparing the real-time carbon emission with the real-time capacity data. The application also includes devices and systems for implementing the methods. The inconvenient problem of measuring of industrial quantity among the prior art is solved to this application.
Description
Technical Field
The application relates to the technical field of intelligent detection, in particular to an industrial carbon emission online detection method and device.
Background
Industry is an important foundation for economic development and is also one of the major coal consumption and pollutant emission industries. The industrial energy resource consumption is high, the environment is heavy, the energy conservation and emission reduction requirements exist, and further, an industrial energy conservation and emission reduction statistics, monitoring and assessment system is needed. The method has the advantages of researching energy conservation and emission reduction, realizing energy conservation and emission reduction, excavating the potential of people and machines, achieving the aims of high coal energy conversion rate, low cost, coal consumption saving and less emission, and having important significance. In addition, as industrial information is distributed on a plurality of information platforms, the observable information quantity is small or incomplete, the comprehensive condition of production is difficult to master, and in addition, part of industrial quantity cannot be directly measured, so that related data such as coal consumption and carbon emission cannot be accurately monitored in real time, and the improvement of energy conservation and emission reduction capability is influenced.
Disclosure of Invention
The application provides an industrial carbon emission online detection method and device, which solve the problem that industrial quantity is inconvenient to measure, and particularly solve the defects that the existing carbon emission detection equipment is difficult to collect data and is difficult to popularize and apply in a large range. The scheme of this application is particularly useful for monitoring industrial area carbon emission.
The embodiment of the application provides an industrial carbon emission online detection method, which comprises the following steps:
monitoring industrial gas emission and detecting the concentration of carbon element related gas;
calculating the real-time carbon emission according to the gas concentration and the flow;
and acquiring real-time capacity data of a plant area, and comparing the real-time carbon emission with the real-time capacity data.
Preferably, further comprising the steps of: and determining whether the variation of the real-time carbon emission exceeds a second set range or not in response to the fact that the variation of the real-time capacity data is smaller than the first set range.
Preferably, before the steps, the method further comprises the following steps: and setting the unit time length of the real-time carbon emission and/or the real-time capacity data statistic value.
Preferably, comparing the real-time carbon emissions with the real-time capacity data further comprises: performing correlation analysis on the real-time carbon emission data and the real-time capacity data; it is determined whether the value of the correlation coefficient is smaller than a set first threshold value, or whether the variation value of the correlation coefficient is larger than a set second threshold value.
Further preferably, historical data of equipment faults, historical data of real-time carbon emission and historical data of real-time capacity data are collected, a prediction model is built, the result of comparison between the current real-time carbon emission and the real-time capacity data is processed, and the equipment faults are predicted.
The embodiment of the application also provides an industrial carbon emission online detection device, which is used for realizing the method in any embodiment of the application, and the device comprises a gas detection module, a flow detection module and a processing module; the gas detection module is used for collecting carbon element related gas sensing information; the flow detection module is used for collecting the gas flow; and the processing module is used for calculating the real-time carbon emission.
Preferably, the processing module is further configured to receive real-time capacity data of a plant area, and compare the real-time carbon emission with the real-time capacity data.
Preferably, the online industrial carbon emission detection device further comprises a communication module for transmitting real-time carbon emission data.
The embodiment of the application also provides an industrial carbon emission online monitoring system, which is used for realizing the method in any embodiment of the application and comprises an industrial carbon emission online detection device and a background server;
the industrial carbon emission online detection device is used for monitoring industrial gas emission and detecting the concentration of carbon element related gas; calculating the real-time carbon emission according to the gas concentration and the flow, and sending the real-time carbon emission data;
and the background server is used for acquiring real-time capacity data of a plant area and comparing the real-time carbon emission with the real-time capacity data.
Preferably, the background server is further configured to collect historical data of equipment failures, historical data of real-time carbon emission and historical data of real-time capacity data, establish a prediction model, process a result of comparison between the current real-time carbon emission and the real-time capacity data, and predict equipment failures.
Embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method according to any of the embodiments of the present application.
Further, an electronic device is provided in an embodiment of the present application, which includes a memory, a processor, and a computer program stored in the memory and executable by the processor, and the processor executes the computer program to implement the steps of the method according to any embodiment of the present application.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
by integrating the correlation judgment of the carbon emission and the capacity data, the fault of the production equipment in the industrial plant can be found in time, and the fault monitoring of the production equipment is indirectly realized.
The system has the advantages that the real-time communication function is provided, the function of detecting the gas related to the carbon element is achieved, meanwhile, the collected concentration information is transmitted to the background, and the carbon emission data are sensed in time.
The carbon discharge capacity is calculated through the laser gas detection module and the flow detection module, the detection flow is simplified, the equipment volume is reduced, and the device is convenient to install at a high-altitude discharge port by adopting a storage battery for power supply.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of an embodiment of the method of the present application;
FIG. 2 is a schematic diagram of an embodiment of the apparatus and system architecture of the present application;
FIG. 3 is a flow chart of another embodiment of a method of operating the apparatus of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flow chart of an embodiment of the method of the present application.
The embodiment of the application provides an industrial carbon emission online detection method, which comprises the following steps:
and 11, setting the unit time length of the real-time carbon emission and the real-time capacity data statistic value.
And step 13, acquiring real-time capacity data of a plant area, and comparing the real-time carbon emission with the real-time capacity data.
Preferably, comparing the real-time carbon emissions with the real-time capacity data further comprises: performing correlation analysis on the real-time carbon emission data and the real-time capacity data; it is determined whether the value of the correlation coefficient is smaller than a set first threshold value or whether the variation value of the correlation coefficient is larger than a set second threshold value.
For another example, in response to the real-time capacity data variation being smaller than the first set range, it is determined whether the real-time carbon emission variation exceeds a second set range.
And step 14, acquiring historical data of equipment faults, historical data of real-time carbon emission and historical data of real-time capacity data, establishing a prediction model, processing a result of comparison between the current real-time carbon emission and the real-time capacity data, and predicting the equipment faults.
The prediction model is trained by using the historical data, and preferably, for example, the model is trained by comparing the historical data of real-time carbon emissions with the historical data of real-time capacity data, using a value of a correlation coefficient or a variation value of the correlation coefficient of the real-time carbon emissions data and the real-time capacity data as characteristic data. For example, a correlation coefficient data set is constituted by a time-varying correlation coefficient and correlation coefficients distributed at a plurality of detection positions. For another example, the model is trained by using an event that the real-time carbon emission amount variation is determined to exceed a second set range in response to the real-time capacity data variation being smaller than the first set range as the characteristic data. For example, the abnormal event data set is constituted by the time distribution and the detection position distribution of the occurrence of the abnormal event, and the amount of change in the real-time carbon emission amount. As another example, historical data of device failures includes the type of device that failed and the type of failure. And constructing a training data set by using historical data of equipment faults, historical data of real-time carbon emission and historical data of real-time capacity data.
And processing the result of comparison between the current real-time carbon emission and the real-time capacity data by using the trained prediction model, and predicting equipment failure.
Fig. 2 is a schematic diagram of an embodiment of the apparatus and system architecture of the present application.
As shown in fig. 2, an online industrial carbon emission detection apparatus 20 is further provided in the embodiment of the present application, for implementing the method according to any embodiment of the present application, and the apparatus includes a gas detection module 21, a flow detection module 25, a processing module 22, and a power supply module 23.
The power supply module supplies power to the equipment.
And the gas detection module is used for collecting the carbon element related gas sensing information. Preferably, a laser is used as the detection means, and the reflected laser spectrum is measured to determine the type and concentration of the elemental carbon-containing gas. The gas detection module is a laser carbon element related gas detection module and is used for realizing the target gas concentration detection function.
The flow detection module is used for collecting the gas flow. In some embodiments, the flow detection module is configured to enable acquisition of vent flow data.
And the processing module is used for calculating the real-time carbon emission. The processing module is the core of the whole equipment, can control other modules to work in order, realizes communication with other equipment, and realizes calculation of concentration values of gases related to carbon elements and real-time report of data; further, the processing module can judge whether the production equipment has faults or not according to the detected change of the carbon element emission.
In some embodiments of further optimization, the processing module is further configured to receive real-time capacity data of a plant area, and compare the real-time carbon emissions with the real-time capacity data.
In some further optimized embodiments, when the abnormal increase or decrease of the carbon element emission is detected, the detection data is uploaded to the background server and compared with the daily capacity of the industrial plant area, and if the capacity is not changed and the abnormal decrease or increase of the carbon emission exists, the production equipment is in fault, so that further overhaul is prompted, and the equipment fault is prevented from further developing.
In a further optimized embodiment, the online industrial carbon emission detection device further comprises a communication module 24 for transmitting real-time carbon emission data. For example, the communication between the device and the background server is realized by using a wireless communication module. Further the wireless communication module employs NB-IoT wireless communication.
In some embodiments, the MCU controls the laser gas detection module to collect the concentration of the gas related to carbon element, the flow detection module is controlled to collect the gas flow, the gas emission is calculated according to the collected gas concentration value and the current flow value, and the NB-IoT module sends the data to the upper computer to record and analyze the data.
As shown in fig. 2, an online monitoring system for industrial carbon emissions is further provided in the embodiment of the present application, for implementing the method according to any embodiment of the present application, including 1 or more online industrial carbon emission detection devices 20, and further including a background server 26;
the industrial carbon emission online detection device is used for monitoring industrial gas emission and detecting the concentration of carbon element related gas; and calculating the real-time carbon emission according to the gas concentration and the flow, and sending the real-time carbon emission data. The device can detect carbon element related gas, and calculate carbon emission according to the flow information that gathers, and data transmit data to the backstage through NB-IoT module in real time, and the industrial factory goes out the erection equipment at every gas discharge port, can realize the regional carbon emission real time monitoring.
And the background server is used for acquiring real-time capacity data of a plant area and comparing the real-time carbon emission with the real-time capacity data.
Preferably, the background server is further configured to collect historical data of equipment faults, historical data of real-time carbon emission and historical data of real-time capacity data, establish a prediction model, process a result of comparison between the current real-time carbon emission and the real-time capacity data, and predict equipment faults.
Other functions of the device (processing module or background server) implementing the method of the present application are described in steps 11 to 14, and are not described herein again.
FIG. 3 is a flow chart of another embodiment of a method of operating the apparatus of the present application.
In order to provide an online carbon emission monitoring method, the invention adopts the following technical scheme:
And 32, detecting the concentration of the carbon element related gas.
And step 33, calculating the real-time carbon emission according to the gas concentration and the flow.
And step 34, comparing the real-time carbon emission with the real-time carbon emission at the same moment in the early stage.
And step 35, when the carbon emission is monitored to be abnormal, comparing the carbon emission with production data of a factory to determine whether the carbon emission is abnormally increased or reduced, further responding to the abnormal increase or reduction of the carbon emission to predict equipment faults, and giving equipment fault prompts if the equipment faults are determined.
And step 36, discharging industrial gas, and continuing to monitor carbon emission by the equipment until the industrial production is stopped on the same day.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application therefore also proposes a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of the embodiments of the present application.
Further, the present application also proposes an electronic device (or computing device) comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method according to any of the embodiments of the present application when executing the computer program.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or 5/block diagrams, and flow and/or ≧ be implemented by computer program instructions
Or a combination of blocks. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising 8230; \8230;" comprises 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises such element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.
Claims (10)
1. An industrial carbon emission online detection method is characterized by comprising the following steps:
monitoring industrial gas emission and detecting the concentration of carbon element related gas;
calculating the real-time carbon emission according to the gas concentration and the flow;
and acquiring real-time capacity data of a plant area, and comparing the real-time carbon emission with the real-time capacity data.
2. The method according to claim 1, wherein the real-time capacity data variation is smaller than a first predetermined range, and determining whether the real-time carbon emission variation exceeds a second predetermined range.
3. The method for detecting industrial carbon emissions on line according to claim 1, further comprising the steps of: and setting the unit time length of the real-time carbon emission and/or the real-time capacity data statistic value.
4. The method of claim 1, wherein comparing the real-time carbon emissions with the real-time capacity data further comprises:
performing correlation analysis on the real-time carbon emission data and the real-time capacity data,
it is determined whether the value of the correlation coefficient is smaller than a set first threshold value or whether the variation value of the correlation coefficient is larger than a set second threshold value.
5. The method according to any one of claims 1 to 4, wherein historical data of equipment faults, historical data of real-time carbon emission and historical data of real-time capacity data are collected, a prediction model is established, and the results of comparison between the current real-time carbon emission and the real-time capacity data are processed to predict the equipment faults.
6. An on-line industrial carbon emission detection device for implementing the method of any one of claims 1 to 5, comprising:
the gas detection module is used for collecting the carbon element related gas sensing information;
the flow detection module is used for collecting the gas flow;
the processing module is used for calculating the real-time carbon emission; and receiving real-time capacity data of the plant area, and comparing the real-time carbon emission with the real-time capacity data.
7. An industrial carbon emission online monitoring system for realizing the method of any one of claims 1 to 5 is characterized by comprising an industrial carbon emission online detection device and a background server;
the industrial carbon emission online detection device is used for monitoring industrial gas emission and detecting the concentration of carbon element related gas; calculating the real-time carbon emission according to the gas concentration and the flow, and sending the real-time carbon emission data;
and the background server is used for acquiring real-time capacity data of a plant area and comparing the real-time carbon emission with the real-time capacity data.
8. The on-line industrial carbon emission monitoring system of claim 7,
the background server is also used for acquiring historical data of equipment faults, historical data of real-time carbon emission and historical data of real-time capacity data and establishing a prediction model; and processing the result of comparison between the current real-time carbon emission and the real-time capacity data, and predicting equipment failure.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
10. An electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method according to any one of claims 1 to 5 when executing the computer program.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202211595074.5A CN115753642A (en) | 2022-12-13 | 2022-12-13 | Industrial carbon emission online detection method and device |
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| CN202211595074.5A CN115753642A (en) | 2022-12-13 | 2022-12-13 | Industrial carbon emission online detection method and device |
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| CN202211595074.5A Pending CN115753642A (en) | 2022-12-13 | 2022-12-13 | Industrial carbon emission online detection method and device |
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