CN116862253A - Multi-source data carbon emission evaluation method and device - Google Patents

Multi-source data carbon emission evaluation method and device Download PDF

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Publication number
CN116862253A
CN116862253A CN202310722785.2A CN202310722785A CN116862253A CN 116862253 A CN116862253 A CN 116862253A CN 202310722785 A CN202310722785 A CN 202310722785A CN 116862253 A CN116862253 A CN 116862253A
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carbon emission
data
carbon
analysis
coal
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邹祥波
熊凯
陈创庭
王海洲
陈公达
张爽
蔡汝金
陈耀荣
丁勇
郑佩璋
叶骥
韦婵婵
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Guangdong Energy Group Co ltd
Zhuhai Special Economic Zone Guangzhu Power Generation Co ltd
Guangdong Energy Group Science And Technology Research Institute Co ltd
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Guangdong Energy Group Co ltd
Zhuhai Special Economic Zone Guangzhu Power Generation Co ltd
Guangdong Energy Group Science And Technology Research Institute Co ltd
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Priority to CN202310722785.2A priority Critical patent/CN116862253A/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/84—Greenhouse gas [GHG] management systems

Abstract

The invention relates to the technical field of carbon emission, and discloses a multi-source data carbon emission evaluation method and device. According to the method, carbon emission data of different data sources are acquired from carbon emission checking, carbon emission on-line monitoring and coal quality quick-detecting equipment respectively, mathematical statistics and detection are carried out on the acquired carbon emission data before data analysis, early warning instructions are started if abnormality is detected, statistical calculation analysis, uncertainty analysis and correlation analysis are carried out on the carbon emission data of three different sources respectively if the carbon emission data passes the inspection, corresponding analysis results are formed, and the analysis results are sent to a large screen to be visualized in the forms of charts, numerical values and the like. The invention can collect carbon emission data of different sources, optimize the carbon emission data, more intuitively embody the carbon emission data of a multi-source scene and increase the richness and the accuracy of the analysis result of the carbon emission data.

Description

Multi-source data carbon emission evaluation method and device
Technical Field
The invention relates to the technical field of carbon emission, in particular to a multi-source data carbon emission evaluation method and device.
Background
The current carbon emission management platform is generally composed of a data acquisition module for recording carbon emission through an on-line monitoring system or manually, a data storage module for storing the acquired carbon emission, a data analysis processing module for calculating and analyzing the carbon emission, and the like. The carbon emission management platform can be used for knowing and comparing the carbon emission generated by each period and each department.
However, the existing carbon emission management platform has a certain limitation, and can only display data of carbon emission check or carbon emission on-line monitoring singly, and lacks a technology for comparing and analyzing multi-source data.
Disclosure of Invention
The invention provides a multi-source data carbon emission evaluation method and device, which can collect different data sources in various modes, monitor and analyze the collected multi-source data in real time, optimize the carbon emission data and increase the richness and accuracy of the analysis result of the carbon emission data.
In order to solve the technical problems, the invention provides a multi-source data carbon emission evaluation method, which comprises the following steps:
collecting carbon emission check data, carbon emission on-line monitoring data and coal quality quick-detection data respectively;
respectively carrying out mathematical statistics and inspection on the collected carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-inspection data, and forming carbon emission check data to be analyzed, carbon emission on-line monitoring data to be analyzed and coal quality quick-inspection data to be analyzed after the inspection is passed;
respectively carrying out statistical calculation analysis on carbon emission check data to be analyzed, carbon emission online monitoring data to be analyzed and coal quality quick detection data to be analyzed to respectively form first carbon emission data, second carbon emission data and third carbon emission data;
Performing uncertainty analysis and correlation analysis on the first carbon emission data, the second carbon emission data and the third carbon emission data respectively to form analysis results;
and sending the analysis result to a display screen for visual display, wherein the display mode comprises graph display and numerical display.
According to the invention, carbon emission data of different data sources are acquired from carbon emission checking, carbon emission on-line monitoring and coal quality quick-detecting equipment respectively, mathematical statistics and inspection are carried out on the acquired carbon emission data before the data are analyzed, statistical calculation analysis, uncertainty analysis and correlation analysis are carried out on the carbon emission data of three different sources respectively after the data pass through the inspection, so that corresponding analysis results are formed, the analysis results are sent to a large screen to be visualized in the forms of graphs, numerical values and the like, so that the carbon emission data of a multi-source scene are more intuitively reflected, and the richness and the accuracy of the analysis results of the carbon emission data are increased.
Further, the collecting of carbon emission check data, carbon emission on-line monitoring data and coal quality quick-check data respectively specifically includes:
determining parameters for checking the carbon emission according to preset carbon emission checking requirements; wherein the parameters for checking the carbon emission include fossil fuel combustion emission parameters and purchase electricity consumption emission parameters;
Collecting carbon emission verification data according to the parameters for verifying the carbon emission;
by CO 2 The analyzer monitors a plurality of parameters in real time to form carbon emission online monitoring data; wherein the parameters include CO 2 Concentration measurement and flue gas flow measurement;
detecting the coal quality by using the coal quality rapid detection equipment to obtain coal quality rapid detection data; the parameters for detecting the coal quality comprise a daily detection parameter of the coal entering the furnace and a batch detection parameter of the coal entering the factory.
The invention determines parameters required to be collected for carbon emission check through the preset carbon emission check requirement, collects carbon emission check data according to the determined parameters, and utilizes CO 2 The analyzer and the coal quality rapid detection device respectively acquire carbon emission online monitoring data and coal quality rapid detection data, so that carbon emission data of different sources are acquired by using 3 different data acquisition methods, and the carbon emission data is optimized.
Further, the collected carbon emission check data, carbon emission on-line monitoring data and coal quality quick-check data are respectively subjected to mathematical statistics test, and specifically:
respectively calculating absolute errors between each parameter data and corresponding historical data in the carbon emission check data, the carbon emission online monitoring data and the coal quality quick detection data, and comparing calculated error results with preset standards;
When the error result of each parameter in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-detection data is smaller than a preset standard, passing the inspection;
and when the error result of any one parameter in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-detection data exceeds a preset standard, determining abnormality and starting an early warning instruction.
The invention carries out mathematical statistics inspection on the collected carbon emission data before analyzing the data, calculates the absolute error between the collected actual measurement data and normal historical data, compares the calculation result with a built-in fixed standard, and can continue to carry out the next analysis processing through inspection if the error of each parameter is smaller than the built-in fixed standard; if one of the parameters exceeds the built-in fixed standard, the system automatically starts an early warning instruction to make an abnormal prompt. The data is checked before the data analysis is carried out, so that abnormal conditions can be avoided, carbon emission data is optimized, and accuracy of data analysis results is improved.
Further, the statistical calculation analysis is performed on the carbon emission check data to be analyzed, the carbon emission on-line monitoring data to be analyzed and the coal quality quick-check data to be analyzed respectively to form first carbon emission data, second carbon emission data and third carbon emission data, which specifically are:
Multiplying the fossil fuel consumption, the elemental carbon content and the carbon oxidation rate in the carbon emission check data to be analyzed to form fossil fuel combustion emission;
multiplying the purchased electricity consumption in the carbon emission check data to be analyzed by the grid emission factor to form the purchased electricity consumption;
adding the fossil fuel combustion emission amount and the purchase use electric power emission amount to form first carbon emission amount data;
calculating the carbon emission amount per hour according to the carbon emission on-line monitoring data to be analyzed;
accumulating and calculating the carbon emission amount per hour to respectively obtain the daily carbon emission amount, the monthly carbon emission amount and the annual carbon emission amount;
combining the daily carbon emission, the monthly carbon emission and the annual carbon emission to form second carbon emission data;
weighting calculation is carried out on daily detection parameters of the coal entering into the furnace in the rapid detection data of the coal quality to be analyzed, so that average elemental carbon content of the coal entering into the furnace and average low-level calorific value of the coal entering into the furnace are obtained;
weighting and calculating each batch of detection parameters of the coal in the coal quality quick detection data to be analyzed, obtaining the average elemental carbon content of the month of the in-and-out coal and the average low-level calorific value of the month of the in-and-out coal;
And combining the average element carbon content of the fired coal month, the average low-level calorific value of the fired coal month, the average element carbon content of the fired coal month and the average low-level calorific value of the fired coal month to form third carbon emission data.
The method comprises the steps of obtaining first carbon emission data from carbon emission check by counting fossil fuel combustion emission and purchasing electricity emission; acquiring second carbon emission data from the carbon emission on-line monitoring by accumulating the carbon emission amount of each hour in the carbon emission on-line monitoring data; and obtaining third carbon emission data from rapid quality detection of coal by calculating the carbon content and low-level heating value of elements of the coal to be fired and the coal to be subjected to factory. According to different data types, the carbon emission data of 3 different sources are respectively subjected to statistical analysis by using different statistical means, and the carbon emission data after 3 kinds of statistical analysis can be obtained. Different statistical methods are selected according to different data types, so that the efficiency and quality of statistical analysis can be improved.
Further, the uncertainty analysis and the correlation analysis are performed on the first carbon emission data, the second carbon emission data and the third carbon emission data respectively to form analysis results, specifically:
Respectively analyzing uncertainty of each parameter in the first carbon emission data, the second carbon emission data and the third carbon emission data;
performing uncertainty component synthesis on the uncertainty of each parameter according to the respective data category to respectively form relative standard uncertainty of the first carbon emission data, the second carbon emission data and the third carbon emission data;
and multiplying the relative standard uncertainty of the first carbon emission data, the second carbon emission data and the third carbon emission data by different factors respectively to form relative expansion uncertainty of the first carbon emission data, the second carbon emission data and the third carbon emission data.
Further, the uncertainty of each parameter in the first carbon emission data, the second carbon emission data and the third carbon emission data is analyzed respectively, specifically:
calculating a first uncertainty of each parameter in the third carbon emission data according to the detection process of each parameter in the third carbon emission data;
calculating a second uncertainty of each parameter in the third carbon emission data according to the coal quality quick detection equipment of each parameter in the third carbon emission data;
and combining the first uncertainty and the second uncertainty of each parameter in the third carbon emission data to form the uncertainty of each parameter in the third carbon emission data.
Further, the uncertainty analysis and the correlation analysis are performed on the first carbon emission data, the second carbon emission data and the third carbon emission data respectively to form analysis results, specifically:
and respectively carrying out pearson correlation analysis on the first carbon emission data, the second carbon emission data and the third carbon emission data and the main influencing factors to obtain a first correlation coefficient, a second correlation coefficient and a third correlation coefficient.
The invention provides a multi-source data carbon emission evaluation method, which is characterized in that carbon emission data of different data sources are respectively obtained from carbon emission checking, carbon emission on-line monitoring and coal quality quick-detecting equipment, the collected carbon emission data is firstly subjected to mathematical statistics checking before the data is analyzed, if abnormal is detected, an early warning instruction is started, if the abnormal is detected, three kinds of carbon emission data of different sources are respectively subjected to statistical calculation analysis, uncertainty analysis and correlation analysis to form corresponding analysis results, and the analysis results are sent to a large screen to be visualized in the forms of charts, numerical values and the like. The invention can collect carbon emission data of different sources, optimize the carbon emission data, more intuitively embody the carbon emission data of a multi-source scene and increase the richness and the accuracy of the analysis result of the carbon emission data.
Correspondingly, the invention provides a multisource data carbon emission evaluation device, which comprises: the device comprises an acquisition module, a detection module, a first analysis module, a second analysis module and a display module;
the acquisition module is used for respectively acquiring carbon emission check data, carbon emission on-line monitoring data and coal quality quick-detection data;
the inspection module is used for respectively carrying out mathematical statistics inspection on the collected carbon emission inspection data, the carbon emission on-line monitoring data and the coal quality quick inspection data, and forming carbon emission inspection data to be analyzed, carbon emission on-line monitoring data to be analyzed and coal quality quick inspection data to be analyzed after the inspection is passed;
the first analysis module is used for respectively carrying out statistical calculation analysis on carbon emission check data to be analyzed, carbon emission online monitoring data to be analyzed and coal quality quick detection data to be analyzed to respectively form first carbon emission data, second carbon emission data and third carbon emission data;
the second analysis module is used for performing uncertainty analysis and correlation analysis on the first carbon emission data, the second carbon emission data and the third carbon emission data respectively to form an analysis result;
the display module is used for sending the analysis result to a display screen for visual display, wherein the display mode comprises graph display and numerical display.
Further, the acquisition module includes: the device comprises a determining unit, a first acquisition unit, a second acquisition unit and a third acquisition unit;
the determining unit is used for determining parameters for checking the carbon emission according to preset carbon emission checking requirements; wherein the parameters for checking the carbon emission include fossil fuel combustion emission parameters and purchase electricity consumption emission parameters;
the first acquisition unit is used for acquiring carbon emission verification data according to the parameters for verifying the carbon emission;
the second acquisition unit is used for utilizing CO 2 The analyzer monitors a plurality of parameters in real time to form carbon emission online monitoring data; wherein the parameters include CO 2 Concentration measurement and flue gas flow measurement;
the third acquisition unit is used for detecting the coal quality by using the coal quality rapid detection equipment to acquire coal quality rapid detection data; the parameters for detecting the coal quality comprise a daily detection parameter of the coal entering the furnace and a batch detection parameter of the coal entering the factory.
Further, the inspection module includes: the device comprises a computing unit, a first judging unit and a second judging unit;
the calculation unit is used for respectively calculating absolute errors between each parameter data and corresponding historical data in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-detection data, and comparing calculated error results with preset standards;
The first judging unit is used for checking when the error result of each parameter in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-checking data is smaller than a preset standard;
and the second judging unit is used for determining abnormality and starting an early warning instruction when the error result of any one parameter in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-detection data exceeds a preset standard.
The invention provides a multi-source data carbon emission evaluation device, which is based on the organic combination among modules, can collect different data sources in a plurality of modes, simultaneously monitors and analyzes the collected multi-source data in real time, optimizes the carbon emission data and increases the richness and accuracy of the analysis result of the carbon emission data.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of a multi-source carbon emission evaluation method according to the present invention;
FIG. 2 is a schematic diagram of an embodiment of a multi-source carbon emission evaluation device according to the present invention;
fig. 3 is a schematic structural diagram of the intelligent carbon emission management platform provided by the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, a flow chart of an embodiment of a multi-source data carbon emission evaluation method provided by the present invention is shown, and the method includes steps 101 to 105, where the steps are specifically as follows:
step 101: and respectively collecting carbon emission check data, carbon emission on-line monitoring data and coal quality quick-check data.
Further, in the first embodiment of the present invention, the collecting of the carbon emission check data, the carbon emission on-line monitoring data, and the coal quality quick-check data respectively specifically includes:
determining parameters for checking the carbon emission according to preset carbon emission checking requirements; wherein the parameters for checking the carbon emission include fossil fuel combustion emission parameters and purchase electricity consumption emission parameters;
collecting carbon emission verification data according to the parameters for verifying the carbon emission;
by CO 2 The analyzer monitors a plurality of parameters in real time to form carbon emission online monitoring data; wherein the parameters include CO 2 Concentration measurement and flue gas flow measurement;
detecting the coal quality by using the coal quality rapid detection equipment to obtain coal quality rapid detection data; the parameters for detecting the coal quality comprise a daily detection parameter of the coal entering the furnace and a batch detection parameter of the coal entering the factory.
In the first embodiment of the present invention, the carbon emission verification data is required to collect key parameters for verifying the carbon emission according to the carbon emission verification and verification guidelines, and the carbon emission generated by the enterprise activities mainly includes fossil fuel combustion emission and purchase electricity emission. Parameters for accounting the combustion emission of the fossil fuel include fossil fuel consumption, low-level heating value, carbon content of unit heating value and carbon oxidation rate; parameters that account for the purchase of electricity usage emissions are the purchase electricity usage and the grid emission factor. The carbon emission on-line monitoring data is derived from CO 2 The analyzer monitors in real time, and the main monitored parameters comprise CO 2 Concentration measurements and flue gas flow measurements, wherein flue gas flow measurements in turn include flue gas temperature, pressure, flow rate, humidity, etc. The coal quality rapid detection data is derived from coal quality rapid detection equipment, wherein the coal quality rapid detection equipment is equipment for rapidly carrying out industrial analysis and elemental analysis on coal quality, and mainly detected parameters comprise the calorific value of the coal quality and the elemental carbon content.
Step 102: and respectively carrying out mathematical statistics and inspection on the collected carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-inspection data, and forming carbon emission check data to be analyzed, carbon emission on-line monitoring data to be analyzed and coal quality quick-inspection data to be analyzed after the inspection is passed.
Further, in the first embodiment of the present invention, the mathematical statistical test is performed on the collected carbon emission check data, the carbon emission online monitoring data, and the coal quality rapid detection data, which specifically are:
respectively calculating absolute errors between each parameter data and corresponding historical data in the carbon emission check data, the carbon emission online monitoring data and the coal quality quick detection data, and comparing calculated error results with preset standards;
When the error result of each parameter in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-detection data is smaller than a preset standard, passing the inspection;
and when the error result of any one parameter in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-detection data exceeds a preset standard, determining abnormality and starting an early warning instruction.
In the first embodiment of the invention, the collected carbon emission data is subjected to mathematical statistics test before the data is analyzed, absolute errors between the collected actual measurement data and normal historical data are calculated, the calculated results are compared with built-in fixed standards, and if the errors of each parameter are smaller than the built-in fixed standards, the next analysis processing can be continuously carried out through the test; if one of the parameters exceeds the built-in fixed standard, the system automatically starts an early warning instruction to make an abnormal prompt.
Step 103: and respectively carrying out statistical calculation analysis on the carbon emission check data to be analyzed, the carbon emission online monitoring data to be analyzed and the coal quality quick detection data to be analyzed to respectively form first carbon emission data, second carbon emission data and third carbon emission data.
Further, in the first embodiment of the present invention, the statistical calculation analysis is performed on the carbon emission check data to be analyzed, the carbon emission online monitoring data to be analyzed, and the coal quality rapid detection data to be analyzed, so as to form first carbon emission data, second carbon emission data, and third carbon emission data, respectively, which specifically are:
multiplying the fossil fuel consumption, the elemental carbon content and the carbon oxidation rate in the carbon emission check data to be analyzed to form fossil fuel combustion emission;
multiplying the purchased electricity consumption in the carbon emission check data to be analyzed by the grid emission factor to form the purchased electricity consumption;
adding the fossil fuel combustion emission amount and the purchase use electric power emission amount to form first carbon emission amount data;
calculating the carbon emission amount per hour according to the carbon emission on-line monitoring data to be analyzed;
accumulating and calculating the carbon emission amount per hour to respectively obtain the daily carbon emission amount, the monthly carbon emission amount and the annual carbon emission amount;
combining the daily carbon emission, the monthly carbon emission and the annual carbon emission to form second carbon emission data;
weighting calculation is carried out on daily detection parameters of the coal entering into the furnace in the rapid detection data of the coal quality to be analyzed, so that average elemental carbon content of the coal entering into the furnace and average low-level calorific value of the coal entering into the furnace are obtained;
Weighting and calculating each batch of detection parameters of the coal in the coal quality quick detection data to be analyzed, obtaining the average elemental carbon content of the month of the in-and-out coal and the average low-level calorific value of the month of the in-and-out coal;
and combining the average element carbon content of the fired coal month, the average low-level calorific value of the fired coal month, the average element carbon content of the fired coal month and the average low-level calorific value of the fired coal month to form third carbon emission data.
In the first embodiment of the invention, statistical calculation analysis is performed on the carbon emission check data to be analyzed, the carbon emission online monitoring data to be analyzed and the coal quality quick detection data to be analyzed respectively, and the method specifically comprises the following steps:
(1) Statistical calculation analysis of carbon emission check data to be analyzed:
in the carbon emission accounting, the carbon emission amount is expressed as the sum of the fossil fuel combustion emission amount and the emission amount generated by purchasing the electricity using the electricity. The fossil fuel combustion emission is calculated by the product of parameters such as fossil fuel consumption, elemental carbon content, carbon oxidation rate and the like; the emissions generated by the purchased power are calculated from the product of the purchased power usage and the grid emissions factor. The specific calculation formula is as follows:
C i ＝NCV i ×CC i
wherein i represents a fossil fuel type code; c (C) i Represents the elemental carbon content of the ith fossil fuel; NCV (NCV) i A low-grade heating value representing the i-th fossil fuel; CC (CC) i Represents the carbon content per unit heating value of the ith fossil fuel.
Wherein i represents a fossil fuel type code; e (E) Combustion process Indicating fossil fuel combustionCarbon emissions of (2); FC (fiber channel) i Represents the i-th consumption of fossil fuel; c (C) i Represents the elemental carbon content of the ith fossil fuel; OF (OF) i Represents the carbon oxidation rate of the ith fossil fuel.
E Purchase electricity ＝AD Electric power ×EF Electric power
Wherein i represents a fossil fuel type code; e (E) Purchase electricity Representing the amount of carbon emissions generated by purchasing electricity; AD (analog to digital) converter Electric power Indicating the amount of electricity purchased and used; EF (electric F) Electric power Representing the grid emission factor.
E＝E Combustion process +E Purchase electricity
Wherein E represents the total amount of carbon emissions in the carbon emissions accounting; e (E) Combustion process Carbon emissions indicative of fossil fuel combustion; e (E) Purchase electricity Representing the amount of carbon emissions generated by purchasing electricity.
(2) Statistical calculation analysis of carbon emission online monitoring data to be analyzed:
carbon emission obtained by on-line monitoring of carbon emission, and key parameters involved include CO in flue gas 2 Concentration, flue gas flow, etc. Wherein the statistical analysis is mainly the accumulated calculation of carbon emission through flue gas CO 2 And calculating the carbon emission of a certain hour by using parameters such as concentration, flue gas flow, temperature, pressure, flow rate and the like, and further calculating the daily carbon emission, the monthly carbon emission and the annual carbon emission by statistics. The specific calculation formula is as follows:
Wherein C is sn Representing CO in standard state 2 Mass concentration; c (C) s Representing CO obtained by on-line monitoring 2 Volume concentration.
Wherein, the liquid crystal display device comprises a liquid crystal display device,representing the average flow velocity of wet flue gas measured by the section flow velocity; KV represents the velocity field coefficient; />The average flow rate of wet flue gas measured in the cross-sectional flow direction is shown.
Wherein Q is S Represents the wet flue gas flow; f represents the measured cross-sectional area;the average flow rate of wet flue gas measured in the cross-sectional flow direction is shown.
Wherein Q is Sn The dry flue gas volume flow under the standard state is represented; q (Q) S Represents the wet flue gas flow; t is t s Representing the temperature of the flue gas; b (B) a Represents atmospheric pressure; p (P) S The static pressure of the flue gas is represented; x is X Sw Indicating the moisture content of the flue gas.
Wherein C is d Representing CO in standard state 2 Dry mass concentration; c (C) W Representing CO in standard state 2 Wet matrix amount concentration; x is X Sw Indicating the moisture content of the flue gas.
G h ＝C d ×Q Sn ×10 ―6
Wherein G is h Representing flue gas CO 2 Discharge mass flow rate; c (C) d Represents the carbon emission amount on that day; q (Q) Sn And the dry flue gas volume flow under the standard state is shown.
Wherein C is d Represents the carbon emission amount on that day; g hi Representing the carbon emissions at the i-th hour of the day.
Wherein G is m Representing the carbon emissions for that month; d (D) m Indicating the number of days of the month; g di Represents the carbon emissions on day i of the month.
Wherein G is y Represents the carbon emissions for that year; d (D) y Representing the number of days of the year; g'. di Representing the carbon emissions on day i of the year.
(2) Statistical calculation analysis of the coal quality rapid detection data to be analyzed:
important parameters related to statistical analysis of the rapid-detection data of the coal quality to be analyzed comprise elemental carbon content and low-order heating value. Taking coal as an example, coal quality detection is developed in the forms of daily detection of coal entering a furnace and batch detection of coal entering a factory. The elemental carbon content is calculated and analyzed by adopting data weight of daily coal feeding detection to obtain the average elemental carbon content of the coal feeding month, and the weight is daily coal feeding consumption; and (5) obtaining the monthly average elemental carbon content of the coal in the factory by adopting the weighted calculation and analysis of the detection data of the coal in the factory in each batch per month, wherein the weighted weight is the receiving amount of the coal in the factory in each batch.
The low-order heating value is obtained by calculating and analyzing a month average low-order heating value weighted average, and the weighted weight is the month consumption of the fire coal. The month average low-level heating value of the coal fed into the furnace is obtained by weighted average calculation and analysis of the low-level heating value of the coal consumed by each day/class, and the weighted weight is the daily coal consumption of each day/class; the month average low-rank heating value of the coal entering the factory is calculated by the weighted average of the average low-rank heating values of each batch, and the weighted weight is the receiving amount of the coal entering the factory from the month batch.
Step 104: and performing uncertainty analysis and correlation analysis on the first carbon emission data, the second carbon emission data and the third carbon emission data respectively to form analysis results.
Further, in the first embodiment of the present invention, uncertainty analysis and correlation analysis are performed on the first carbon emission amount data, the second carbon emission amount data, and the third carbon emission amount data, respectively, to form analysis results, specifically:
respectively analyzing uncertainty of each parameter in the first carbon emission data, the second carbon emission data and the third carbon emission data;
performing uncertainty component synthesis on the uncertainty of each parameter according to the respective data category to respectively form relative standard uncertainty of the first carbon emission data, the second carbon emission data and the third carbon emission data;
and multiplying the relative standard uncertainty of the first carbon emission data, the second carbon emission data and the third carbon emission data by different factors respectively to form relative expansion uncertainty of the first carbon emission data, the second carbon emission data and the third carbon emission data.
Further, in the first embodiment of the present invention, the uncertainty of each parameter in the first carbon emission amount data, the second carbon emission amount data, and the third carbon emission amount data is specifically:
Calculating a first uncertainty of each parameter in the third carbon emission data according to the detection process of each parameter in the third carbon emission data;
calculating a second uncertainty of each parameter in the third carbon emission data according to the coal quality quick detection equipment of each parameter in the third carbon emission data;
and combining the first uncertainty and the second uncertainty of each parameter in the third carbon emission data to form the uncertainty of each parameter in the third carbon emission data.
In the first embodiment of the present invention, uncertainty analysis is performed on the first carbon emission amount data, the second carbon emission amount data, and the third carbon emission amount data, respectively, as follows:
(1) Uncertainty analysis is performed on first carbon emission amount data corresponding to the carbon emission check data:
according to the carbon emission accounting guideline, an emission factor method is adopted to account the carbon emission of fossil fuel combustion, and the related parameters mainly comprise fossil fuel combustion consumption, low-level heating value of fuel, carbon content of unit heat value, carbon oxidation rate and the like. The procedure for the analysis of carbon emission accounting uncertainty was as follows:
1) Firstly, the uncertainty of each measured parameter is analyzed, and the uncertainty mainly comprises fossil fuel combustion consumption, low-level heating value of fuel, carbon content of unit heating value, carbon oxidation rate and the like.
2) Uncertainty component synthesis is then performed. Because the parameters are mutually independent, according to the uncertainty of the components of the measured parameters, the calculation of the synthesis uncertainty of the calculated carbon emission is carried out according to the following formula:
wherein u is r (E Accounting ) A relative standard uncertainty representing the amount of carbon emissions under the carbon emissions accounting; u (u) r (FC) represents a relative standard uncertainty component of fossil fuel combustion consumption; u (u) r (NCV) represents the relative standard uncertainty of the low heat generation of the fuel; u (u) r (CC) represents the relative standard uncertainty of the carbon content per unit heating value; u (u) r (OF) represents the relative standard uncertainty OF the carbon oxidation rate;
3) And finally, calculating to obtain the relative expansion uncertainty of the calculated carbon emission:
U(E accounting )＝k 1 u r (E Accounting )
Wherein U (E) Accounting ) Representing a relative expansion uncertainty of the carbon emission amount; k1 represents an inclusion factor accounting for the carbon emission amount; u (u) r (E Accounting ) Relative scale indicating carbon emission under carbon emission accountingQuasi uncertainty.
(2) Uncertainty analysis is carried out on second carbon emission data corresponding to the carbon emission online monitoring data:
the uncertainty source of the carbon emission on-line monitoring is mainly composed of flue gas flow measurement and CO 2 Concentration measurements, while gas flow measurements involve numerous parameters including flow rate, temperature, pressure, etc., the uncertainty of these parameters is synthesized to yield the uncertainty of the gas flow measurement. CO 2 The uncertainty sources of concentration measurement are mainly instrument measurement errors and the like. The on-line monitoring of carbon emission uncertainty analysis includes the following steps:
1) First of all, the uncertainty of each measured parameter is analyzed, mainly comprising CO 2 Concentration, flue gas flow, pressure, temperature, water content, etc.
2) Uncertainty component synthesis is then performed. Because the parameters are mutually independent, according to the uncertainty of the components of the measured parameters, the calculation of the synthesis uncertainty of the calculated carbon emission is carried out according to the following formula:
wherein u is rel (M(CO 2 ) Representing the on-line monitored relative uncertainty of carbon emission synthesis; u (u) rel (C(CO 2 ) (2) represents CO 2 Relative uncertainty of concentration; u (u) rel (Q V ) An uncertainty representing the flow of flue gas; u (u) rel (C(H 2 O)) represents the relative uncertainty of the moisture content of the flue gas; u (u) rel (p) represents the relative uncertainty of the flue gas pressure; u (u) rel (T) represents the relative uncertainty of the flue gas temperature; u (u) rel And (t) represents the relative uncertainty of incomplete temporal coverage.
3) And finally, calculating to obtain the relative expansion uncertainty of the online monitoring carbon emission:
U(E actual measurement )＝k 2 u rel (M(CO 2 ))
Wherein U (E) Actual measurement ) Representing carbon emissionsRelative expansion uncertainty; k (k) 2 A inclusion factor indicative of on-line monitoring of carbon emissions; u (u) rel (M(CO 2 ) Indicating the relative uncertainty of the on-line monitored carbon emission synthesis.
(3) Uncertainty analysis is carried out on third carbon emission data corresponding to the coal quality quick detection data:
the low-grade heating value and the elemental carbon content of the coal can be obtained rapidly through the coal rapid detection equipment, then the carbon emission of the fossil fuel combustion is calculated according to a formula in the carbon emission accounting guide, and the related parameters mainly comprise the low-grade heating value and the elemental carbon content of the coal, the fossil fuel combustion consumption, the carbon oxidation rate and the like, which are measured through the coal rapid detection equipment.
The uncertainty of the measurement of the carbon emission of the coal quality rapid detection equipment consists of two parts: the first part is the uncertainty (class a uncertainty) caused by the repeated test procedure of each parameter; the second part is uncertainty (class B uncertainty) caused by the coal quality quick-test equipment itself to the measurement result.
The method for analyzing the uncertainty of the measurement carbon emission of the coal quality rapid detection equipment comprises the following steps:
1) The uncertainty caused by the repeated test procedure of each parameter is first analyzed, and then the class a uncertainty is calculated according to the following formula. The related parameters mainly comprise the low-grade calorific value and the elemental carbon content of the coal quality, the fossil fuel combustion consumption, the carbon oxidation rate and the like, which are measured by the coal quality rapid detection equipment.
Wherein u is r (A) Class a relative standard uncertainty representing the carbon emission of the coal quality rapid inspection equipment; u (u) r (FC) represents a relative standard uncertainty component of fossil fuel combustion consumption; u (u) r (C) Representing the relative standard uncertainty of the elemental carbon content measured by the coal quality rapid detection equipment; u (u) r (OF) represents the relative standard uncertainty OF the carbon oxidation rate.
Then the coal quality quick-detecting equipment is analyzed according to the following calculation formulaUncertainty u caused by measurement result r (B)：
Wherein u is r (B) Class B relative standard uncertainty representing the carbon emission of the coal quality rapid detection equipment; alpha represents measured error values of various influencing factors; k (k) 3 And the inclusion factor representing the uncertainty of the class B relative standard of the carbon emission of the coal quality rapid detection equipment.
2) Uncertainty component synthesis is performed. Because each parameter is mutually independent, according to the uncertainty of each measured parameter component, the calculation of the uncertainty of the synthesis of the measured carbon emission of the coal quality rapid detection equipment is carried out according to the following formula:
wherein u is c,r Representing the synthetic uncertainty of the coal quality rapid detection equipment for measuring the carbon emission; u (u) r (A) Class a relative standard uncertainty representing the carbon emission of the coal quality rapid inspection equipment; u (u) r (B) Class B relative standard uncertainty representing the carbon emissions of a coal quality rapid test device.
3) And finally, calculating to obtain the relative expansion uncertainty of the coal quality rapid detection equipment to measure the carbon emission:
U(E quick detection of coal quality )＝k 4 u c,r
Wherein U (E) Quick detection of coal quality ) Representing the relative expansion uncertainty of the coal quality rapid detection equipment for measuring the carbon emission; k (k) 4 A factor representing the carbon emission of the coal quality rapid detection equipment; u (u) c,r Indicating the synthetic uncertainty of the coal quality rapid detection equipment in measuring the carbon emission.
Further, in the first embodiment of the present invention, uncertainty analysis and correlation analysis are performed on the first carbon emission amount data, the second carbon emission amount data, and the third carbon emission amount data, respectively, to form analysis results, specifically:
and respectively carrying out pearson correlation analysis on the first carbon emission data, the second carbon emission data and the third carbon emission data and the main influencing factors to obtain a first correlation coefficient, a second correlation coefficient and a third correlation coefficient.
In the first embodiment of the present invention, correlation analysis is performed on the first carbon emission data, the second carbon emission data, and the third carbon emission data, respectively, and the correlation analysis is mainly used for analyzing the carbon emission check data, the carbon emission on-line monitoring data, the coal quality quick-check data, and the correlations between the carbon emission and the main influencing factors calculated by the respective analysis, and the correlations are quantitatively expressed mainly by Pearson (Pearson) correlation coefficients. The Pearson (Pearson) correlation coefficient is suitable for measuring the correlation of two numerical variables, wherein the numerical variables comprise distance and fixed ratio variables, and the numerical variables are characterized in that the values of the variables are represented by numbers and can be subjected to addition and subtraction operation to calculate the difference. Let two random variables x and y, the correlation coefficient of the variable population is as follows:
Wherein cov (x, y) represents the covariance of variable x and variable y;representing the variance of the variable x;representing the variance of the variable y; ρ represents the overall correlation coefficient.
Step 105: and sending the analysis result to a display screen for visual display, wherein the display mode comprises graph display and numerical display.
In the first embodiment of the present invention, the analysis results may be compared and analyzed with respect to the carbon emission data of three sources in the form of a graph, so as to find out a period, division or generation link in which the carbon emission is high. The multi-source carbon emission parameter data of carbon emission check, carbon emission on-line monitoring and coal quality quick detection can be displayed by connecting the large screen, and the multi-source carbon emission parameter data is used for visualizing the data of processing analysis in the forms of charts, numerical values and the like, so that the carbon emission data of a multi-source scene can be more intuitively embodied.
In summary, the first embodiment of the present invention provides a multi-source data carbon emission evaluation method, which obtains carbon emission data of different data sources from carbon emission check, carbon emission online monitoring and coal quality rapid inspection equipment, performs mathematical statistics check on the collected carbon emission data before analyzing the data, starts an early warning instruction if abnormality is detected, performs statistical calculation analysis, uncertainty analysis and correlation analysis on three kinds of carbon emission data of different sources if the abnormality is detected, forms a corresponding analysis result, and sends the analysis result to a large screen to visualize in the form of a graph, a numerical value and the like. The invention can collect carbon emission data of different sources, optimize the carbon emission data, more intuitively embody the carbon emission data of a multi-source scene and increase the richness and the accuracy of the analysis result of the carbon emission data.
Example 2
Referring to fig. 2, a schematic structural diagram of an embodiment of a multi-source data carbon emission evaluation device provided by the present invention includes an acquisition module 201, a test module 202, a first analysis module 203, a second analysis module 204, and a display module 205;
the acquisition module 201 is used for respectively acquiring carbon emission check data, carbon emission online monitoring data and coal quality rapid detection data;
the inspection module 202 is used for respectively carrying out mathematical statistics inspection on the collected carbon emission inspection data, the carbon emission on-line monitoring data and the coal quality quick inspection data, and forming carbon emission inspection data to be analyzed, carbon emission on-line monitoring data to be analyzed and coal quality quick inspection data to be analyzed after the inspection is passed;
the first analysis module 203 is configured to perform statistical calculation and analysis on the carbon emission check data to be analyzed, the carbon emission online monitoring data to be analyzed, and the coal quality quick detection data to be analyzed, to form first carbon emission data, second carbon emission data, and third carbon emission data, respectively;
the second analysis module 204 is configured to perform uncertainty analysis and correlation analysis on the first carbon emission data, the second carbon emission data, and the third carbon emission data, respectively, to form an analysis result;
The display module 205 is configured to send the analysis result to a display screen for visual display, where the display mode includes graphic display and numerical display.
Further, in the second embodiment of the present invention, the acquisition module 201 includes: the device comprises a determining unit, a first acquisition unit, a second acquisition unit and a third acquisition unit;
the determining unit is used for determining parameters for checking the carbon emission according to preset carbon emission checking requirements; wherein the parameters for checking the carbon emission include fossil fuel combustion emission parameters and purchase electricity consumption emission parameters;
the first acquisition unit is used for acquiring carbon emission verification data according to the parameters for verifying the carbon emission;
the second acquisition unit is used for utilizing CO 2 The analyzer monitors a plurality of parameters in real time to form carbon emission online monitoring data; wherein the parameters include CO 2 Concentration measurement and flue gas flow measurement;
the third acquisition unit is used for detecting the coal quality by using the coal quality rapid detection equipment to acquire coal quality rapid detection data; the parameters for detecting the coal quality comprise a daily detection parameter of the coal entering the furnace and a batch detection parameter of the coal entering the factory.
Further, in a second embodiment of the present invention, the inspection module 202 includes: the device comprises a computing unit, a first judging unit and a second judging unit;
The calculating unit is used for calculating absolute errors between each parameter data and corresponding historical data in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-detection data respectively, and comparing calculated error results with preset standards;
the first judging unit is used for checking when the error result of each parameter in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-checking data is smaller than a preset standard;
and the second judging unit is used for determining abnormality and starting an early warning instruction when the error result of any one parameter in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-detection data exceeds a preset standard.
Further, in a second embodiment of the present invention, the first analysis module includes: a combustion emission amount calculation unit, an electric power emission amount calculation unit, a first formation unit, a carbon emission amount calculation unit, an accumulation calculation unit, a second formation unit, a first weight calculation unit, a second weight calculation unit, and a third formation unit;
the combustion emission amount calculating unit is used for multiplying the fossil fuel consumption amount, the elemental carbon content and the carbon oxidation rate in the carbon emission check data to be analyzed to form fossil fuel combustion emission amount;
The electric power emission amount calculation unit is used for multiplying the purchased electricity consumption amount in the carbon emission check data to be analyzed by the electric network emission factor to form the purchased electricity consumption amount;
a first forming unit for adding the fossil fuel combustion emission amount and the purchase use electric power emission amount to form first carbon emission amount data;
the carbon emission amount calculating unit is used for calculating the carbon emission amount per hour according to the carbon emission on-line monitoring data to be analyzed;
the accumulation calculation unit is used for carrying out accumulation calculation on the carbon emission amount in each hour to respectively obtain daily carbon emission amount, monthly carbon emission amount and annual carbon emission amount;
a second formation calculation unit for combining the daily carbon emission amount, the monthly carbon emission amount, and the annual carbon emission amount to form second carbon emission amount data;
the first weighting calculation unit is used for carrying out weighting calculation on the daily detection parameters of the coal entering into the furnace in the rapid detection data of the coal quality to be analyzed to obtain the average elemental carbon content of the month of the coal entering into the furnace and the average low-level calorific value of the month of the coal entering into the furnace;
the second weighting calculation unit is used for carrying out weighting calculation on each batch of detection parameters of the coal in the coal quality quick detection data to be analyzed, obtaining the average elemental carbon content of the month of the in-and-out coal and the average low-level calorific value of the month of the in-and-out coal;
And the third forming unit is used for combining the average element carbon content of the month of the coal entering the furnace, the average low-level heating value of the month of the coal entering the furnace, the average element carbon content of the month of the coal entering the factory and the average low-level heating value of the month of the coal entering the factory to form third carbon emission data.
Further, in a second embodiment of the present invention, the second analysis module includes: an uncertainty analysis unit, a synthesis unit and an expansion unit;
the uncertainty analysis unit is used for respectively analyzing the uncertainty of each parameter in the first carbon emission data, the second carbon emission data and the third carbon emission data;
the synthesizing unit is used for carrying out uncertainty component synthesis on the uncertainty of each parameter according to respective data types to respectively form relative standard uncertainty of the first carbon emission data, the second carbon emission data and the third carbon emission data;
the expansion unit is used for multiplying the relative standard uncertainty of the first carbon emission data, the second carbon emission data and the third carbon emission data by different factors respectively to form the relative expansion uncertainty of the first carbon emission data, the second carbon emission data and the third carbon emission data.
Further, in a second embodiment of the present invention, the uncertainty analysis unit includes: a first computing subunit, a second computing subunit, and a forming subunit;
the first calculating subunit is used for calculating the first uncertainty of each parameter in the third carbon emission data according to the detection process of each parameter in the third carbon emission data;
the second calculating subunit is used for calculating a second uncertainty of each parameter in the third carbon emission data according to the coal quality quick-detecting equipment of each parameter in the third carbon emission data;
the forming subunit is configured to combine the first uncertainty and the second uncertainty of each parameter in the third carbon emission data to form an uncertainty of each parameter in the third carbon emission data.
Further, in a second embodiment of the present invention, the second analysis module further includes: a correlation analysis unit;
the correlation analysis unit is used for performing pearson correlation analysis on the first carbon emission data, the second carbon emission data and the third carbon emission data and the main influence factors respectively to obtain a first correlation coefficient, a second correlation coefficient and a third correlation coefficient.
In summary, the second embodiment of the present invention provides a multi-source data carbon emission evaluation device, based on the organic combination among modules, respectively obtaining carbon emission data of different data sources from carbon emission checking, carbon emission on-line monitoring and coal quality rapid inspection equipment, performing mathematical statistics checking on the collected carbon emission data before analyzing the data, if abnormality is detected, starting an early warning instruction, if the abnormality is detected, respectively performing statistical calculation analysis, uncertainty analysis and correlation analysis on the three carbon emission data of different sources, forming corresponding analysis results, and transmitting the analysis results to a large screen to perform visualization in the form of graphs, numerical values and the like. The invention can collect carbon emission data of different sources, optimize the carbon emission data, more intuitively embody the carbon emission data of a multi-source scene and increase the richness and the accuracy of the analysis result of the carbon emission data.
Example 3
Referring to fig. 3, a schematic structural diagram of the carbon emission intelligent management platform provided by the invention includes a data acquisition module, a data storage module, a data processing analysis module and a data display module. The four modules of the carbon emission intelligent management platform are sequentially connected through a communication network.
The data acquisition module mainly comprises a carbon emission on-line monitoring unit, a carbon emission checking unit and a coal quality rapid analysis unit, and aims to acquire data sources from three different parties.
The carbon emission checking unit acquires key parameters of the carbon emission amount according to the carbon emission accounting and checking guideline requirements, and the carbon emission amount generated by enterprise activities mainly comprises fossil fuel combustion emission and purchase electricity emission. Parameters for accounting the combustion emission of the fossil fuel include fossil fuel consumption, low-level heating value, carbon content of unit heating value and carbon oxidation rate; parameters that account for the purchase of electricity usage emissions are the purchase electricity usage and the grid emission factor. And uploading the carbon emission key parameters to the carbon emission checking unit by field personnel in a manual input mode.
The data of the carbon emission on-line monitoring unit is derived from CO 2 The analyzer monitors in real time, and the main monitored parameters comprise CO 2 Concentration measurements and flue gas flow measurements, wherein flue gas flow measurements in turn include flue gas temperature, pressure, flow rate, humidity, etc.
The data of the coal quality rapid analysis unit are derived from coal quality rapid detection equipment, the coal quality rapid detection equipment is equipment for rapidly carrying out industrial analysis and element analysis on coal quality, and mainly detected parameters comprise coal quality heating value and element carbon content, and the parameters of the coal quality rapid detection are uploaded to the coal quality rapid analysis unit through data connection.
The data storage module is used for extracting carbon emission data from the data acquisition module and carrying out storage backup on the carbon emission data.
The data analysis processing module consists of a data statistics calculation unit, a chart analysis unit and a data comparison verification unit.
The data statistics calculation unit is used for further analyzing carbon emission accounting data, coal quality quick detection data and carbon emission online monitoring data, and mainly comprises data statistics analysis, uncertainty analysis, correlation analysis and the like.
The chart analysis unit is used for comparing and analyzing the carbon emission data of three sources through a chart form according to the calculation result, so as to find out a time period, a department or a generation link with higher carbon emission.
The data comparison and verification unit is used for carrying out mathematical statistics and verification on various collected data, and the method for verifying the data abnormality is to calculate absolute errors between the collected actual measurement data and normal historical data and compare the calculation result with a built-in fixed standard. If the error of each parameter is smaller than the built-in fixed standard, the data is free from abnormality, and the next analysis processing is continued; if the error of one parameter exceeds the built-in fixed standard, the system automatically starts an early warning instruction, makes a data abnormality prompt, and retransmits the abnormal data back to the data storage module.
The data display module is connected with the large screen to display multi-source carbon emission parameter data of carbon emission check, carbon emission on-line monitoring and coal quality quick detection, and is used for visualizing the processed and analyzed data in the forms of charts, numerical values and the like, so that the carbon emission data of the multi-source scene is more intuitively embodied.
In summary, the third embodiment of the invention provides a carbon emission intelligent management platform, which is used for respectively acquiring carbon emission data of different data sources from carbon emission checking, carbon emission on-line monitoring and coal quality quick-detecting equipment, carrying out mathematical statistics checking on the acquired carbon emission data before analyzing the data, starting an early warning instruction if abnormality is detected, carrying out statistical calculation analysis, uncertainty analysis and correlation analysis on the carbon emission data of three different sources respectively if the abnormality is detected, forming corresponding analysis results, and sending the analysis results to a large screen to be visualized in the forms of charts, numerical values and the like. The invention can collect carbon emission data of different sources, optimize the carbon emission data, more intuitively embody the carbon emission data of a multi-source scene and increase the richness and the accuracy of the analysis result of the carbon emission data.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention, and are not to be construed as limiting the scope of the invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.

Claims (10)

1. A multi-source data carbon emission evaluation method, comprising:
collecting carbon emission check data, carbon emission on-line monitoring data and coal quality quick-detection data respectively;
respectively carrying out mathematical statistics and inspection on the collected carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-inspection data, and forming carbon emission check data to be analyzed, carbon emission on-line monitoring data to be analyzed and coal quality quick-inspection data to be analyzed after the inspection is passed;
respectively carrying out statistical calculation analysis on carbon emission check data to be analyzed, carbon emission online monitoring data to be analyzed and coal quality quick detection data to be analyzed to respectively form first carbon emission data, second carbon emission data and third carbon emission data;
Performing uncertainty analysis and correlation analysis on the first carbon emission data, the second carbon emission data and the third carbon emission data respectively to form analysis results;
and sending the analysis result to a display screen for visual display, wherein the display mode comprises graph display and numerical display.
2. The multi-source data carbon emission evaluation method according to claim 1, wherein the collecting of carbon emission check data, carbon emission on-line monitoring data and coal quality quick-check data respectively comprises:
determining parameters for checking the carbon emission according to preset carbon emission checking requirements; wherein the parameters for checking the carbon emission include fossil fuel combustion emission parameters and purchase electricity consumption emission parameters;
collecting carbon emission verification data according to the parameters for verifying the carbon emission;
by CO 2 The analyzer monitors a plurality of parameters in real time to form carbon emission online monitoring data; wherein the parameters include CO 2 Concentration measurement and flue gas flow measurement;
detecting the coal quality by using the coal quality rapid detection equipment to obtain coal quality rapid detection data; the parameters for detecting the coal quality comprise a daily detection parameter of the coal entering the furnace and a batch detection parameter of the coal entering the factory.
3. The multi-source data carbon emission evaluation method according to claim 1, wherein the collected carbon emission check data, carbon emission on-line monitoring data and coal quality rapid-detection data are respectively subjected to mathematical statistics, specifically:
respectively calculating absolute errors between each parameter data and corresponding historical data in the carbon emission check data, the carbon emission online monitoring data and the coal quality quick detection data, and comparing calculated error results with preset standards;
when the error result of each parameter in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-detection data is smaller than a preset standard, passing the inspection;
and when the error result of any one parameter in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-detection data exceeds a preset standard, determining abnormality and starting an early warning instruction.
4. The multi-source data carbon emission evaluation method according to claim 1, wherein the statistical calculation analysis is performed on the carbon emission check data to be analyzed, the carbon emission on-line monitoring data to be analyzed, and the coal quality quick-check data to be analyzed, respectively, to form first carbon emission data, second carbon emission data, and third carbon emission data, respectively, specifically:
Multiplying the fossil fuel consumption, the elemental carbon content and the carbon oxidation rate in the carbon emission check data to be analyzed to form fossil fuel combustion emission;
multiplying the purchased electricity consumption in the carbon emission check data to be analyzed by the grid emission factor to form the purchased electricity consumption;
adding the fossil fuel combustion emission amount and the purchase use electric power emission amount to form first carbon emission amount data;
calculating the carbon emission amount per hour according to the carbon emission on-line monitoring data to be analyzed;
accumulating and calculating the carbon emission amount per hour to respectively obtain the daily carbon emission amount, the monthly carbon emission amount and the annual carbon emission amount;
combining the daily carbon emission, the monthly carbon emission and the annual carbon emission to form second carbon emission data;
weighting calculation is carried out on daily detection parameters of the coal entering into the furnace in the rapid detection data of the coal quality to be analyzed, so that average elemental carbon content of the coal entering into the furnace and average low-level calorific value of the coal entering into the furnace are obtained;
weighting and calculating each batch of detection parameters of the coal in the coal quality quick detection data to be analyzed, obtaining the average elemental carbon content of the month of the in-and-out coal and the average low-level calorific value of the month of the in-and-out coal;
And combining the average element carbon content of the fired coal month, the average low-level calorific value of the fired coal month, the average element carbon content of the fired coal month and the average low-level calorific value of the fired coal month to form third carbon emission data.
5. The multi-source data carbon emission evaluation method according to claim 1, wherein the uncertainty analysis and the correlation analysis are performed on the first carbon emission data, the second carbon emission data, and the third carbon emission data, respectively, to form an analysis result, specifically:
respectively analyzing uncertainty of each parameter in the first carbon emission data, the second carbon emission data and the third carbon emission data;
performing uncertainty component synthesis on the uncertainty of each parameter according to the respective data category to respectively form relative standard uncertainty of the first carbon emission data, the second carbon emission data and the third carbon emission data;
and multiplying the relative standard uncertainty of the first carbon emission data, the second carbon emission data and the third carbon emission data by different factors respectively to form relative expansion uncertainty of the first carbon emission data, the second carbon emission data and the third carbon emission data.
6. The multi-source data carbon emission evaluation method according to claim 5, wherein the uncertainty of each parameter in the first carbon emission amount data, the second carbon emission amount data, and the third carbon emission amount data is specifically:
calculating a first uncertainty of each parameter in the third carbon emission data according to the detection process of each parameter in the third carbon emission data;
calculating a second uncertainty of each parameter in the third carbon emission data according to the coal quality quick detection equipment of each parameter in the third carbon emission data;
and combining the first uncertainty and the second uncertainty of each parameter in the third carbon emission data to form the uncertainty of each parameter in the third carbon emission data.
7. The multi-source data carbon emission evaluation method according to claim 1, wherein the uncertainty analysis and the correlation analysis are performed on the first carbon emission data, the second carbon emission data, and the third carbon emission data, respectively, to form an analysis result, specifically:
and respectively carrying out pearson correlation analysis on the first carbon emission data, the second carbon emission data and the third carbon emission data and the main influencing factors to obtain a first correlation coefficient, a second correlation coefficient and a third correlation coefficient.
8. A multi-source data carbon emission evaluation device, comprising: the device comprises an acquisition module, a detection module, a first analysis module, a second analysis module and a display module;
the acquisition module is used for respectively acquiring carbon emission check data, carbon emission on-line monitoring data and coal quality quick-detection data;
the inspection module is used for respectively carrying out mathematical statistics inspection on the collected carbon emission inspection data, the carbon emission on-line monitoring data and the coal quality quick inspection data, and forming carbon emission inspection data to be analyzed, carbon emission on-line monitoring data to be analyzed and coal quality quick inspection data to be analyzed after the inspection is passed;
the first analysis module is used for respectively carrying out statistical calculation analysis on carbon emission check data to be analyzed, carbon emission online monitoring data to be analyzed and coal quality quick detection data to be analyzed to respectively form first carbon emission data, second carbon emission data and third carbon emission data;
the second analysis module is used for performing uncertainty analysis and correlation analysis on the first carbon emission data, the second carbon emission data and the third carbon emission data respectively to form an analysis result;
the display module is used for sending the analysis result to a display screen for visual display, wherein the display mode comprises graph display and numerical display.
9. The multi-source data carbon emission assessment device of claim 8, wherein the acquisition module comprises: the device comprises a determining unit, a first acquisition unit, a second acquisition unit and a third acquisition unit;
the determining unit is used for determining parameters for checking the carbon emission according to preset carbon emission checking requirements; wherein the parameters for checking the carbon emission include fossil fuel combustion emission parameters and purchase electricity consumption emission parameters;
the first acquisition unit is used for acquiring carbon emission verification data according to the parameters for verifying the carbon emission;
the second acquisition unit is used for utilizing CO 2 The analyzer monitors a plurality of parameters in real time to form carbon emission online monitoring data; wherein the parameters include CO 2 Concentration measurement and flue gas flow measurement;
the third acquisition unit is used for detecting the coal quality by using the coal quality rapid detection equipment to acquire coal quality rapid detection data; the parameters for detecting the coal quality comprise a daily detection parameter of the coal entering the furnace and a batch detection parameter of the coal entering the factory.
10. The multi-source data carbon emission evaluation device of claim 8, wherein the inspection module comprises: the device comprises a computing unit, a first judging unit and a second judging unit;
The calculation unit is used for respectively calculating absolute errors between each parameter data and corresponding historical data in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-detection data, and comparing calculated error results with preset standards;
the first judging unit is used for checking when the error result of each parameter in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-checking data is smaller than a preset standard;
and the second judging unit is used for determining abnormality and starting an early warning instruction when the error result of any one parameter in the carbon emission check data, the carbon emission on-line monitoring data and the coal quality quick-detection data exceeds a preset standard.
CN202310722785.2A 2023-06-16 2023-06-16 Multi-source data carbon emission evaluation method and device Pending CN116862253A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TWI841496B (en) 2023-10-13 2024-05-01 艾爾法數位股份有限公司 Pre-check system of a greenhouse gas digital platform and its use method

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TWI841496B (en) 2023-10-13 2024-05-01 艾爾法數位股份有限公司 Pre-check system of a greenhouse gas digital platform and its use method

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