CN114611934A - Iron and steel enterprise full-process carbon emission accounting method based on Gaussian distribution fitting and rolling correction - Google Patents
Iron and steel enterprise full-process carbon emission accounting method based on Gaussian distribution fitting and rolling correction Download PDFInfo
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- 238000005259 measurement Methods 0.000 claims description 21
- 239000000047 product Substances 0.000 claims description 18
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- 230000005611 electricity Effects 0.000 claims description 10
- 239000006227 byproduct Substances 0.000 claims description 9
- 239000003245 coal Substances 0.000 claims description 8
- 239000008188 pellet Substances 0.000 claims description 8
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- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 4
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- 229910052760 oxygen Inorganic materials 0.000 claims description 4
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- 238000004939 coking Methods 0.000 abstract description 3
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- 238000005245 sintering Methods 0.000 abstract description 3
- 238000009628 steelmaking Methods 0.000 abstract 1
- UHOVQNZJYSORNB-UHFFFAOYSA-N Benzene Chemical compound C1=CC=CC=C1 UHOVQNZJYSORNB-UHFFFAOYSA-N 0.000 description 6
- 239000000571 coke Substances 0.000 description 3
- ODINCKMPIJJUCX-UHFFFAOYSA-N Calcium oxide Chemical compound [Ca]=O ODINCKMPIJJUCX-UHFFFAOYSA-N 0.000 description 2
- 235000019738 Limestone Nutrition 0.000 description 2
- 239000003575 carbonaceous material Substances 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000012467 final product Substances 0.000 description 2
- 239000005431 greenhouse gas Substances 0.000 description 2
- 239000006028 limestone Substances 0.000 description 2
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- 230000006978 adaptation Effects 0.000 description 1
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- 239000004568 cement Substances 0.000 description 1
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- 239000002817 coal dust Substances 0.000 description 1
- 230000002153 concerted effect Effects 0.000 description 1
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- 238000007599 discharging Methods 0.000 description 1
- 239000010459 dolomite Substances 0.000 description 1
- 229910000514 dolomite Inorganic materials 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
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- 239000002803 fossil fuel Substances 0.000 description 1
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- 238000002955 isolation Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012946 outsourcing Methods 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- -1 sintered ore Substances 0.000 description 1
- 239000002893 slag Substances 0.000 description 1
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Abstract
The invention discloses a steel enterprise whole-process carbon emission accounting method based on Gaussian distribution fitting and rolling correction. The method comprises the following steps: detecting and collecting component data of raw materials and products used in main production processes (sintering, pelletizing, coking, ironmaking and steelmaking) of an integrated iron and steel enterprise; performing Gaussian distribution fitting processing and rolling iteration on data in a database based on Matlab to obtain the mean value and the variance of the component content of the material; calculating carbon emission factors of all materials and error ranges thereof according to the difference of the carbon content of the materials and the content of other substances; and calculating the carbon emission of the process according to the actual consumption of the raw materials of each main process, and further obtaining the carbon emission of the whole steel process and the error range thereof. The method can effectively solve the problems that the carbon dioxide emission calculation in the production process of the iron and steel enterprise is not targeted, has large fluctuation and is difficult to fit with reality, and has the capability of processing a large amount of data measured data and calculating the carbon emission in real time.
Description
Technical Field
The invention belongs to the field of soft measurement, and particularly relates to a steel enterprise whole-process carbon emission accounting method based on Gaussian distribution fitting and rolling correction.
Background
The environmental problems such as global warming and the like are increasingly highlighted, and under the background, countries in the world reduce the emission of greenhouse gases in a concerted mode, and the countries make a commitment of '3060'. In the energy consumption structure of China, the industry is the backbone industry of national economy, the carbon dioxide emission is the largest, the carbon emission of the iron and steel industry in the industry is only inferior to that of the power industry, and CO is discharged as early as 20182The emission amount reaches 1884.4 million tons, and occupies industrial CO2The total discharge amount reaches 18.72%.
At present, the mainstream carbon dioxide calculation modes in the steel production process at home and abroad mainly comprise a WSA method, an IPCC method and a greenhouse gas emission accounting method and a report guideline of Chinese steel production enterprises, the calculation modes of the three methods are different greatly and are carbon emission calculation based on carbon emission factors and material quantity, and certain improved methods proposed based on the methods follow the basic principle. Therefore, in the actual carbon emission measurement and calculation process, the measurement and calculation accuracy of the carbon emission factor is extremely important.
The carbon emission factor of the material adopted by the enterprise in the process of calculating the carbon dioxide emission in the production process is provided for a third party or is obtained by detecting the components of the actual material, the carbon emission factor has the advantages of low acquisition cost, convenience in calculation and the like, but the actual carbon emission is difficult to reflect in the production process of different production enterprises; the latter is gradually adopted by various enterprises along with the popularization of various real-time detection technologies, but the isolation between the current multiple carbon emission factor measurement and calculation is not improved. Although a large amount of material composition data can be obtained through real-time detection, a plurality of carbon emission factors calculated by a group of data cannot be considered, the carbon emission factors are relatively independent after being measured and determined, the past measurement results are not inherited, and the subsequent measurement is not corrected, so that the actual carbon dioxide emission factors in the steel production process are difficult to reflect.
The method can effectively solve the problems that carbon dioxide emission calculation in the production process of steel enterprises is not targeted, has large fluctuation and is difficult to fit with reality, has the capability of processing a large amount of data measured data and calculating the carbon emission in real time, and has extremely good adaptability to real-time carbon emission measurement and calculation based on big data.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides the steel enterprise full-flow carbon emission accounting method based on Gaussian distribution fitting and rolling correction, which can effectively overcome the problems that carbon dioxide emission calculation in the production process of the steel enterprise is not targeted, has large fluctuation and is difficult to fit with reality, has the capability of processing a large amount of data actual measurement data and calculating the carbon emission in real time, and has excellent adaptability to real-time carbon emission calculation based on large data.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
a steel enterprise full-process carbon emission accounting method based on Gaussian distribution fitting and rolling correction comprises the following steps:
step 1, establishing a database of raw materials and products of each process with pertinence according to different raw material proportions and products in different production enterprise processes, and collecting component measurement data of the databases;
the acquisition periods of different materials are possibly different, and part of the materials are subjected to real-time component measurement by adopting a PGNAA technology, so that components such as iron ore, pellet ore, sinter ore, coal powder and the like can be subjected to online component detection, errors are effectively reduced, and meanwhile, component information can be rapidly obtained in a large quantity; other materials adopt a traditional laboratory component analysis mode, have a long period and have certain hysteresis.
In order to ensure the accuracy of calculation, materials with large carbon emission in the whole production flow are determined, and the sampling period is shortened as much as possible; and the carbon emission factor is smaller, the influence on the whole carbon emission is small, the sampling period can be properly prolonged, and the error caused by measurement is reduced.
Step 2, building Gaussian distribution fitting models of various raw materials in different procedures according to the collected data and iterating;
based on a Matlab platform, a mean value and a variance are obtained by fitting Gaussian distribution of collected data, wherein the mean value is used as a material component proportion calculated by a carbon emission factor, and the variance is used for estimating errors of carbon emission calculation.
Setting the capacity of a data set of various material components, inputting all new data before the collection quantity reaches the upper limit, and performing normal distribution fitting according to the existing data after each collection to obtain a mean value and a variance; after the collection quantity reaches the upper limit, iterative updating is carried out after data collection every time, the oldest data are banned, normal distribution fitting is carried out on the existing data after recording every time, and iterative updating of raw material components is achieved.
Step 3, referring to a carbon emission calculation framework of a WSA method, calculating carbon emission factors aiming at actual material components in different production processes by adopting a carbon mass balance method so as to obtain more accurate carbon emission;
the calculation method is as follows:
step 3.1: calculating a direct carbon emission factor;
the direct carbon emissions include carbon emissions of carbon-containing substances and carbon-containing energy sources, the carbon emission factor of which is determined by the following equation:
wherein, EFiIs the carbon emission factor of item i in tCO2/t；(C)iIs the carbon content of item i in%.
Step 3.2: calculating an indirect carbon emission factor;
the indirect carbon emission comprises power consumption and carbon emission of partial auxiliary materials, such as carbon emission caused by electricity, oxygen and other gases, but the carbon emission site is not in the integrated iron and steel enterprises, so the indirect carbon emission belongs to the indirect carbon emission. Since the gas production process is mostly produced by consuming electricity, it can be calculated by the electrical carbon emission factor.
According to the area of the actual electricity, combining the power carbon emission factor, the indirect carbon emission factor of the rest materials can be obtained, and the formula is as follows:
EFi＝EFe·Qei
in the formula, EFiIs the carbon emission factor of item i in tCO2/t；EFeIs the local grid carbon emission factor, Unit, tCO2/MWh；QeiThe unit is the power consumption of the ith product and the unit is MWh/t.
Step 3.3: calculating a deduction carbon emission factor;
in the carbon flow model, part of carbon substances entering the iron and steel combination is discharged to the environment in the form of carbon dioxide, and the other part of carbon substances leaves the iron and steel combination in the form of products and byproducts of various processes or is recycled, such as product sinter, pellet ore or various coal gases as byproducts. The specific calculation is similar to the calculation of the direct carbon emission factor.
Step 4, substituting the carbon emission factor into each process to calculate the carbon emission;
the carbon emission calculation formula is as follows:
CE＝∑EFi direct×Mi direct+∑EFi e×Mi e-∑EFi p×Mi pwherein, EFi directAnd Mi directRespectively representing the direct carbon emission factor and the corresponding mass, EFi eAnd Mi eRespectively representing the indirect carbon emission factor and its corresponding mass, EFi pAnd Mi pRespectively representing the withholding carbon emission factor and the corresponding quality.
Has the advantages that: compared with the prior art, the invention has the following effective effects:
(1) the problems that carbon dioxide emission calculation in the production process of iron and steel enterprises is not targeted, large in fluctuation and difficult to fit practically are effectively solved;
(2) the method has the capability of processing a large amount of data measured data and calculating the carbon emission in real time, and has excellent adaptability to real-time carbon emission measurement and calculation based on big data.
Drawings
FIG. 1 is a flow chart of the calculation of the present invention.
Detailed Description
The present invention is further illustrated by the following description in conjunction with the accompanying drawings and the specific embodiments, it is to be understood that these examples are given solely for the purpose of illustration and are not intended as a definition of the limits of the invention, since various equivalent modifications will occur to those skilled in the art upon reading the present invention and fall within the limits of the appended claims.
A steel enterprise full-process carbon emission accounting method based on Gaussian distribution fitting and rolling correction comprises the following steps:
step I, constructing a corresponding material database based on the actual steel production process
The whole production process of the integrated iron and steel enterprise mainly comprises five working procedures, namely sintering, pelletizing, coking, a blast furnace and a converter, wherein the working procedures correspond to different main raw materials.
The main raw materials of the sintering process comprise dolomite, quicklime, iron ore, coke and the like, and the final product is sinter and a byproduct thereof; the main raw materials of the pelletizing procedure comprise iron ore fine powder, a binder, an additive, water and the like, and the final product is pellet and a byproduct thereof; the main raw materials of the coking process comprise various coals and coal gases, and the products comprise coke, crude benzene, tar and the like; the blast furnace process has the main raw materials of pellet, sintered ore, coke, iron ore and various gases, and the products of the blast furnace process mainly comprise molten iron and blast furnace gas; the main raw materials of the converter procedure are molten iron, limestone and various ingredients, and the final products are molten steel, converter gas and the like.
Aiming at different raw material ratios and products in different production enterprise flows, a targeted raw material and product database of each procedure is established, and component measurement data of the database is collected.
The acquisition periods of different materials are possibly different, and part of the materials are subjected to real-time component measurement by adopting a PGNAA technology, so that components such as iron ore, pellet ore, sinter ore, coal powder and the like can be subjected to online component detection, errors are effectively reduced, and meanwhile, component information can be rapidly obtained in a large quantity; other materials adopt a traditional laboratory component analysis mode, have a long period and have certain hysteresis.
In order to ensure the accuracy of calculation, it should be determined that materials with large carbon emission such as various coal dust and ore occupy in the whole production process have large carbon emission factors and large carbon emission compared with limestone, oxygen, blast air and the like, and for such materials, the sampling period should be shortened as much as possible, while for materials with small carbon emission factors and small influence on the whole carbon emission, the sampling period can be properly prolonged, and errors caused by measurement can be reduced.
Generally, different sampling periods can make the period of the subsequent calculation have choice, the shorter calculation period can quickly reflect the change of the raw materials, the calculation load is correspondingly increased, and the sampling period is properly selected according to actual conditions.
Step II, building Gaussian distribution fitting models of various raw materials of different procedures according to the collected data and iterating
In the historical production data of the iron and steel combination enterprises, certain data fluctuation exists in the component proportion of each batch of materials obtained based on measurement, and in the calculation of the carbon emission factor, the component proportion of each material needs to be a fixed value, so the data fitting is carried out on the historical production data to obtain the statistical component proportion of each material which can be used. In reality, the component ratio of each material with a fixed source fluctuates, but the uncertainty is mainly composed of various unrelated random factors, so that the gaussian distribution is suitable for such a situation.
Gaussian distributions, also known as normal distributions, are of great importance in many areas of mathematics, physics, and engineering, as well as in statistics. The probability density function of the one-dimensional gaussian distribution and the multi-dimensional gaussian distribution is shown below.
Wherein X and X represent one-dimensional variables and multidimensional variables, mu represents one-dimensional mean and vector mean, and sigma represents2And Σ represents the variance.
Based on a Matlab platform, a mean value and a variance are obtained by fitting Gaussian distribution of collected data, wherein the mean value is used as a material component proportion calculated by a carbon emission factor, and the variance is used for estimating errors of carbon emission calculation.
Setting the capacity of a data set of various material components, inputting all new data before the collection quantity reaches the upper limit, and performing normal distribution fitting according to the existing data after each collection to obtain a mean value and a variance; after the collection quantity reaches the upper limit, iterative updating is carried out after data collection every time, the oldest data are banned, normal distribution fitting is carried out on the existing data after recording every time, and iterative updating of raw material components is achieved. The flow chart is shown in figure 1.
The size of the database is determined according to actual operation conditions, the smaller database can adapt to rapid changes of the material components, the larger database can stabilize fluctuation of the material components, and errors of component measurement caused by measurement contingency are reduced.
Step III, determining corresponding carbon emission factors based on various material components
Referring to a carbon emission calculation framework of a WSA method, a carbon quality balance method is adopted, and a carbon emission factor is calculated according to actual material components in different production processes, so that more accurate carbon emission is obtained.
(1) Direct carbon emission factor determination
Direct carbon emissions include carbon emissions of carbonaceous matter and carbonaceous energy sources, the carbon emission factor of which is determined by the following equation:
wherein, EFiIs the carbon emission factor of item i in tCO2T; (C) i is the carbon content of item i in%.
For example, a coal carbon content of 63% would correspond to a carbon emission factor of 2.31tCO2/t。
(2) Indirect carbon emission factor determination
The indirect carbon emission comprises power consumption and carbon emission of partial auxiliary materials, such as carbon emission caused by electricity, oxygen and other gases, but the carbon emission site is not in the integrated iron and steel enterprises, so the indirect carbon emission belongs to the indirect carbon emission. Since the gas production process is mostly produced by consuming electricity, it can be calculated by the electrical carbon emission factor. Table 1 shows the carbon emission factor under the OM calculation condition of each large power grid at present.
TABLE 1 Power carbon emission factor
| Name of electric network | EFgrid(tCO2/MWh) |
| North China power grid | 0.9437 |
| Northeast electric network | 1.0886 |
| China east electric network | 0.7888 |
| China power grid | 0.8444 |
| Northwest electric network | 0.8990 |
| Southern power grid | 0.8139 |
According to the area of the actual electricity, combining the power carbon emission factor, the indirect carbon emission factor of the rest materials can be obtained, and the formula is as follows:
EFi＝EFe·Qei (4)
in the formula, EFiIs the carbon emission factor of item i in tCO2/t；EFeIs the local grid carbon emission factor, Unit, tCO2/MWh；QeiThe unit is the power consumption of the ith product and the unit is MWh/t.
Table 2 shows the reference carbon emission factors calculated based on the east china power grid for a part of the materials, and the specific results should be combined with the actual results.
TABLE 2 Indirect carbon emission factor Table for part of materials
(3) Deduction carbon emission factor determination
In the carbon flow model, part of the carbon material entering the integrated iron and steel plant is discharged into the environment in the form of carbon dioxide, and the other part of the carbon material leaves the integrated iron and steel plant in the form of products and byproducts of various processes or is recycled, such as product sintered ore, pellet ore or various coal gases as byproducts. The specific calculation method is similar to the calculation of the direct carbon emission factor, and therefore, the detailed description thereof is omitted.
Step IV, substituting the carbon emission factor into each process to calculate the carbon emission
The basic principle of carbon dioxide emissions according to the principle of the WSA process is shown below.
ECO2Either direct discharge or indirect discharge or offset discharge (5)
Wherein direct emission refers to CO emitted during production as a source of emission owned or controlled by a reporting entity2. Namely CO generated by consumption of fossil fuel, flux and the like of enterprises2And (5) discharging. Indirect emissions refer to emissions used by the reporting entity for activity, but emissions occur from emissions sources controlled by other entities. For example, the emission of electricity is calculated from the CO generated by the power generation department when outsourcing power is used by the iron and steel enterprises2The discharge is indirect discharge. The retained discharge refers to CO caused by the use of byproducts (such as tar and crude benzene) or metallurgical slag as cement2The amount of emission right deduction.
And (3) calculating the carbon emission:
CE＝∑EFi direct×Mi direct+∑EFi e×Mi e-∑EFi p×Mi p (6)
wherein, EFi directAnd Mi directRespectively representing the direct carbon emission factor and the corresponding mass, EFi eAnd Mi eRespectively representing the indirect carbon emission factor and its corresponding mass, EFi pAnd Mi pRespectively representing the withholding carbon emission factor and the corresponding quality.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (1)
1. A steel enterprise whole-process carbon emission accounting method based on Gaussian distribution fitting and rolling correction is characterized by comprising the following steps;
step 1, establishing a database of raw materials and products of each process with pertinence according to different raw material proportions and products in different production enterprise processes, and collecting component measurement data of the databases;
the acquisition periods of different materials are possibly different, and part of the materials are subjected to real-time component measurement by adopting a PGNAA technology, so that components such as iron ore, pellet ore, sinter ore, coal powder and the like can be subjected to online component detection, errors are effectively reduced, and meanwhile, component information can be rapidly obtained in a large quantity; other materials adopt a traditional laboratory component analysis mode, have a long period and have certain hysteresis.
In order to ensure the accuracy of calculation, materials with large carbon emission in the whole production flow are determined, and the sampling period is shortened as much as possible; and the carbon emission factor is smaller, the influence on the whole carbon emission is small, the sampling period can be properly prolonged, and the error caused by measurement is reduced.
Step 2, building Gaussian distribution fitting models of various raw materials in different procedures according to the collected data and iterating;
based on a Matlab platform, a mean value and a variance are obtained by fitting Gaussian distribution of collected data, wherein the mean value is used as a material component proportion calculated by a carbon emission factor, and the variance is used for estimating errors of carbon emission calculation.
Setting the capacity of a data set of various material components, inputting all new data before the collection quantity reaches the upper limit, and performing normal distribution fitting according to the existing data after each collection to obtain a mean value and a variance; after the collection quantity reaches the upper limit, iterative updating is carried out after data collection every time, the oldest data are banned, normal distribution fitting is carried out on the existing data after recording every time, and iterative updating of raw material components is achieved.
Step 3, referring to a carbon emission calculation framework of a WSA method, calculating carbon emission factors aiming at actual material components in different production processes by adopting a carbon mass balance method so as to obtain more accurate carbon emission;
the calculation method is as follows:
step 3.1: calculating a direct carbon emission factor;
the direct carbon emissions include carbon emissions of carbon-containing substances and carbon-containing energy sources, the carbon emission factor of which is determined by the following equation:
wherein, EFiIs the carbon emission factor of item i in tCO2/t；(C)iIs the carbon content of item i in%.
Step 3.2: calculating an indirect carbon emission factor;
the indirect carbon emission comprises power consumption and carbon emission of partial auxiliary materials, such as carbon emission caused by electricity, oxygen and other gases, but the carbon emission site is not in the integrated iron and steel enterprises, so the indirect carbon emission belongs to the indirect carbon emission. Since the gas production process is mostly produced by consuming electricity, it can be calculated by the electrical carbon emission factor.
According to the area of the actual electricity, combining the power carbon emission factor, the indirect carbon emission factor of the rest materials can be obtained, and the formula is as follows:
EFi＝EFe·Qei
in the formula, EFiIs the carbon emission factor of item i in tCO2/t；EFeIs the local grid carbon emission factor, Unit, tCO2/MWh；QeiThe unit is the power consumption of the ith product and the unit is MWh/t.
Step 3.3: calculating a deduction carbon emission factor;
in the carbon flow model, part of carbon substances entering the iron and steel combination is discharged to the environment in the form of carbon dioxide, and the other part of carbon substances leaves the iron and steel combination in the form of products and byproducts of various processes or is recycled, such as product sinter, pellet ore or various coal gases as byproducts. The specific calculation is similar to the calculation of the direct carbon emission factor.
Step 4, substituting the carbon emission factor into each process to calculate the carbon emission;
the carbon emission calculation formula is as follows:
CE＝∑EFi direct×Mi direct+∑EFi e×Mi e-∑EFi p×Mi p
wherein, EFi directAnd Mi directRespectively representing the direct carbon emission factor and the corresponding mass, EFi eAnd Mi eRespectively representing the indirect carbon emission factor and its corresponding mass, EFi pAnd Mi pRespectively representing the withholding carbon emission factor and the corresponding quality.
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Citations (2)
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| CN106529732A (en) * | 2016-11-18 | 2017-03-22 | 国网山东省电力公司电力科学研究院 | Carbon emission efficiency prediction method based on neural network and random frontier analysis |
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