A localized coal-fired carbon emission precise accounting method, system and electronic device

Abstract

The application discloses a kind of localized coal-fired carbon emission accurate accounting method, system and electronic equipment, it is related to carbon emission accounting technical field.The method collects the coal production area main coal mine different block coal sample determined according to preset screening standard and tests low calorific value, carbon oxidation rate and carbon content, combined with block reserves and recovery rate weighted coal mine coal-fired carbon emission factor is obtained, and coal-fired carbon emission factor library is established;According to the coal production place of coal-using enterprise, coal mine enterprise name and furnace coal blending ratio matching corresponding coal mine factor, the localized coal-fired carbon emission factor is calculated, and the coal-fired carbon emission amount is obtained in combination with coal consumption amount.The method does not need to collect furnace coal sample every day, can take into account coal mine block coal quality difference and enterprise coal blending randomness, reduce accounting cost and improve accounting accuracy.

Description

Technical Field

[0001] This invention relates to the field of carbon emission accounting technology, specifically to the carbon emission accounting of coal combustion in coal-fired enterprises, and particularly to a localized method, system, and electronic equipment for accurate carbon emission accounting of coal combustion. Background Technology

[0002] Current coal-fired carbon emission accounting primarily relies on the emission factor method. The basic idea of ​​the emission factor method is to multiply activity level data by an emission factor to obtain the carbon emission amount. While this method has a clear structure, the way the emission factor is obtained significantly affects the accounting results. If the emission factor is obtained through high-frequency field measurements, continuous sampling, reduction, and testing of the coal fed into the furnace are required, along with regular measurements of parameters such as calorific value and carbon content. This necessitates significant investment in manpower, equipment, and testing costs for enterprises.

[0003] If companies do not conduct actual measurements, they typically have to use the default emission factors specified in the standards. The advantage of default values ​​is their ease of use; however, there are differences in coal rock quality between different coal-producing regions, different coal mines, and different coal blocks. Furthermore, coal-fired power plants blend coal according to calorific value, inventory, and production needs after purchasing it. A single default value cannot reflect these differences, easily leading to systematic biases in the calculation results. Especially in scenarios where coal from multiple mines is mixed and fed into the furnace within the same company, directly using a fixed default value weakens the impact of the coal source structure and blending ratio on emission levels.

[0004] Existing technologies include schemes that calculate or correct carbon emission factors for coal-fired power plants based on daily coal input testing parameters and continuous flue gas monitoring data. These schemes typically emphasize real-time performance and monitoring correction, but still rely on daily coal quality testing or continuous flue gas monitoring data, resulting in high testing frequency, significant investment in monitoring equipment, and strong dependence on continuous monitoring conditions. Other schemes estimate dynamic emission factors through coal type classification, elemental carbon content prediction, lower heating value prediction, or unit operation data models. While these schemes reduce some testing workload, the quality of coal input to the furnace is random when the coal is blended, and the model predictions are easily affected by historical samples, the accuracy of coal type classification, and differences in operating conditions.

[0005] Therefore, a method is needed that is based on actual coal rock and quality data from coal mines in coal-producing areas, reflects the differences between coal blocks within the mines, and allows for localized accounting by coal-using enterprises, taking into account coal sources and blending ratios. This method should not be simply equivalent to daily measured coal input to the furnace, nor should it rely solely on fixed prediction models. Instead, it should construct a step-by-step accounting path from coal mine blocks to coal input to the coal-using enterprises to resolve the contradiction between low cost and high accuracy. Summary of the Invention

[0006] Technical problems to be solved

[0007] The technical problem this invention aims to solve is that in existing carbon emission accounting for coal-fired enterprises, the high-frequency measurement method of coal fed into the furnace is costly, the default emission factor is difficult to reflect local coal quality differences, and the prediction model is difficult to adapt to the random changes in coal quality after coal blending. Based on these problems, this invention provides a localized method, system, and electronic equipment for accurate carbon emission accounting for coal-fired power plants. This allows the accounting process to utilize coal rock and coal quality data from major coal mines in the coal-producing region, while also incorporating the actual coal source and blending ratio of the coal-using enterprise, thereby improving accounting accuracy at a lower cost.

[0008] Technical solution

[0009] To address the aforementioned technical problems, the technical solution proposed in this invention does not directly apply a single emission factor to enterprise accounting. Instead, it first establishes a coal-fired carbon emission factor database at the coal mine level, and then performs coal source matching and coal blending ratio weighting at the enterprise level. This technical solution includes regional coal mine data acquisition, sample testing and data processing, determination of emission factors for specific blocks, establishment of a coal-fired carbon emission factor database for coal mines, enterprise coal source matching, coal blending ratio processing, localized factor calculation, and emission accounting.

[0010] On the regional coal mine side, geological surveys, data collection, and sample collection from different blocks were conducted on the major coal mines in the coal-producing areas identified according to preset screening criteria to obtain data on coal petrology and quality, production status, reserves, and recovery rates. The preset screening criteria were: based on the coal purchase volume or coal consumption of the coal-using enterprises to be accounted for during the accounting period, coal mines were ranked from highest to lowest coal supply volume, and those with a cumulative coal supply volume accounting for more than 80% of the total coal consumption in the accounting period were selected as the major coal mines in the coal-producing area. Experimental tests were conducted on coal samples to obtain lower heating value, carbon oxidation rate, and carbon content. Subsequently, based on the emission factors of different blocks, and combined with the block reserves and recovery rate, different blocks within the same coal mine were weighted to obtain a coal-fired carbon emission factor that represents the characteristics of the coal supplied by that coal mine. The coal-fired carbon emission factors of multiple major coal mines together form a coal-fired carbon emission factor database for the coal-producing area.

[0011] On the coal-consuming enterprise side, information on the enterprise's coal sources is obtained, including the coal production location, the name of the coal mining enterprise, and the quantity of coal purchased. This coal source information is then matched with coal mine records in the coal-fired carbon emission factor database. Further information on the enterprise's coal blending is obtained, including the amount of coal used in terms of calorific value during the accounting period and the proportion of coal from different coal mines during the blending process. Based on the matched coal-fired carbon emission factors and blending ratios from different coal mines, the localized coal-fired carbon emission factor for the coal-consuming enterprise is calculated, and then the coal-fired carbon emissions are calculated based on the aforementioned coal usage.

[0012] The core of this technical solution lies in forming a two-level weighted path: the first level of weighting is completed on the coal mine side, using block reserves and recovery rates to reflect the contribution of different blocks of the same coal mine to the coal supply; the second level of weighting is completed on the enterprise side, using the proportion of coal from different coal mines used in the coal fed into the furnace to reflect the enterprise's coal blending structure. Through two-level weighting, the method can simultaneously reflect the differences in coal quality at the coal production end and the differences in coal blending at the coal consumption end.

[0013] To ensure the method operates stably in actual enterprise accounting, this invention can also be configured with anomaly verification and update processing logic. Anomaly verification includes checking whether the coal source matches the factor library, whether the coal blending ratio is complete, whether reserves are greater than 0, whether the recovery rate is greater than 0 and not greater than 100%, whether the lower heating value is greater than 0, whether the carbon oxidation rate is greater than 0 and not greater than 100%, and whether the carbon content is greater than 0. Update processing includes updating the factor library or enterprise-localized factors when changes occur in basic coal mine data, annual reserve reports, recovery rate data, or enterprise coal blending conditions.

[0014] Based on the same inventive concept, this invention also provides a localized coal-fired carbon emission accurate accounting system, including a regional coal mine data acquisition module, a sample testing and data processing module, a block factor determination module, a coal mine factor library establishment module, an enterprise source matching module, a coal blending ratio processing module, a localized factor calculation module, and an emission accounting module.

[0015] The regional coal mine data acquisition module is used to acquire basic regional coal mine data of major coal mines in the coal-producing area and record the correlation between coal samples from different blocks and their corresponding blocks. The sample testing and data processing module is used to receive the test results of the lower heating value, carbon oxidation rate, and carbon content of coal samples from different blocks and form a coal petrology and coal quality dataset. The block factor determination module is used to determine the corresponding block emission factor based on the lower heating value, carbon oxidation rate, and carbon content of each block. The coal mine factor library establishment module is used to weight the block emission factors of the same coal mine based on the reserves and recovery rate of each block to obtain the coal mine fuel efficiency. The system establishes a coal carbon emission factor database for coal-producing regions. The enterprise source matching module matches corresponding coal mine carbon emission factors based on coal origin and mine enterprise name. The coal blending ratio processing module determines the proportion of coal from different mines used in the furnace feed. The localization factor calculation module weights the matched coal mine carbon emission factors according to the usage ratio to obtain the localized carbon emission factors for coal-using enterprises. The emission calculation module determines the coal carbon emissions based on the calorific value of coal used within the calculation period and the localized carbon emission factors.

[0016] Based on the same inventive concept, the present invention also provides an electronic device, including a processor, a memory, and a data interface, wherein the memory stores a calculation program that can be executed by the processor.

[0017] The data interface is used to receive basic data of regional coal mines, test results of coal samples from different blocks, coal source data, coal blending data, and coal consumption of coal-using enterprises to be accounted for. When the processor executes the accounting program, it determines the block emission factor based on the test results of coal samples from different blocks, determines the coal mine carbon emission factor based on the block reserves and recovery rate, and establishes a coal-producing area carbon emission factor database. It matches the corresponding coal mine carbon emission factor based on the coal source data of the coal-using enterprises to be accounted for, determines the localized carbon emission factor based on the usage ratio of coal from different coal mines, and determines the carbon emission amount based on the amount of coal used in calorific value during the accounting period.

[0018] Beneficial effects

[0019] Compared with existing technologies, this invention provides a localized method, system, and electronic device for accurate calculation of carbon emissions from coal combustion, which has the following beneficial effects:

[0020] 1. This invention eliminates the need for daily coal sample collection and testing, avoiding tedious monthly operations such as reducing coal content and measuring calorific value and carbon content, thus reducing the manpower and material costs of carbon emission accounting for enterprises.

[0021] 2. This invention calculates the coal combustion carbon emission factor of coal mines based on the coal rock and coal quality, reserves and recovery rate of different blocks in major coal mines. It can reflect the differences in coal quality and the contribution of recoverable reserves in different blocks within the coal mine, and overcome the systematic error caused by coal quality differences due to the default value method.

[0022] 3. This invention calculates localized coal-fired carbon emission factors based on the coal source and coal blending situation of coal-using enterprises. It can adapt to the random changes in the coal quality of coal entering the furnace after coal blending, and avoid the emission factor deviation caused by simply relying on fixed coal types or historical prediction models.

[0023] 4. The accounting path of this invention is clear, forming a traceable data chain. There are clear input-output relationships between coal mine block data, coal mine factor library, enterprise coal source, coal blending ratio, localization factors, and final emissions, which facilitates internal auditing, third-party verification, and subsequent updates for enterprises. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the overall process of the localized coal-fired carbon emission accurate accounting method of the present invention;

[0025] Figure 2 This is a schematic diagram illustrating the process of acquiring regional coal mine data, testing samples, and forming coal rock and coal quality datasets according to the present invention.

[0026] Figure 3 This is a schematic diagram of the block emission factor determination and weighted calculation process of coal mine coal combustion carbon emission factors in this invention;

[0027] Figure 4 This is a schematic diagram of the data structure and enterprise source matching field of the coal-fired carbon emission factor database for coal-producing areas in this invention;

[0028] Figure 5 This is a schematic diagram of the coal source matching and coal blending ratio processing flow for coal-using enterprises according to the present invention.

[0029] Figure 6 This is a schematic diagram of the localized coal-fired carbon emission factor and coal-fired carbon emission calculation process of the present invention.

[0030] Figure 7 This is a schematic diagram of the anomaly verification, factor library update, and accounting report traceability process of this invention. Detailed Implementation

[0031] The present invention will be further described below with reference to embodiments. It should be understood that the following embodiments are used to explain the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention. Without departing from the essence of the present invention, any equivalent substitutions made by those skilled in the art regarding data sources, field naming, database carriers, program implementation methods, or report output formats should fall within the scope of protection of the present invention.

[0032] Example 1: Main System Flow and Module Connection Relationships

[0033] This embodiment provides a localized, precise carbon emission accounting system for coal-fired enterprises. The system uses data from major coal mines in coal-producing areas, determined according to preset screening criteria, as upstream input, and coal source and blending data from the coal-consuming enterprises to be accounted for as downstream input. It connects upstream and downstream data through a coal mine factor library and outputs localized carbon emission factors and carbon emission amounts.

[0034] Reference Appendix Figure 1The regional coal mine data acquisition module takes as input geological survey data, coal petrology and quality data, coal mine production data, reserve data, and coal samples from different blocks of major coal mines in the coal-producing region, determined according to preset screening criteria. Its processing logic is as follows: first, coal mines are sorted from highest to lowest based on their coal purchase volume or coal consumption in the accounting period. Those mines whose cumulative coal supply accounts for more than 80% of the total coal consumption in the accounting period are selected as the major coal mines in the coal-producing region. Then, information such as coal origin, coal mining company name, block division, reserves, and recovery rate are collected around these major coal mines, while coal samples from different blocks are also collected. The output of this module is regional coal mine basic data and a coal sample list. The technical function of this module is to ensure that the subsequent factor library construction no longer relies on a single default value, but has a reproducible local coal mine source basis.

[0035] The sample testing and data processing module takes as input the coal sample and corresponding block information output by the regional coal mine data acquisition module. This module tests the sample for lower heating value, carbon oxidation rate, and carbon content, and correlates the test results with data such as coal mine name, coal origin, block identification, reserves, and recovery rate. The module outputs a coal petrology and quality dataset. If discrepancies occur, such as inconsistencies between sample identification and block data, missing test results, or inconsistent data units, the module outputs an error message requiring data verification or reprocessing. The technical function of this module is to transform the test results of physical samples into computable structured data.

[0036] Reference Appendix Figure 2 The block factor determination module takes as input the lower heating value, carbon oxidation rate, and carbon content of different blocks in the coal rock and coal quality dataset. The module's processing logic follows the calculation approach for coal combustion carbon emission factors, converting carbon content and carbon oxidation rate into carbon dioxide emission contributions, and using the lower heating value as the unit heat conversion benchmark to obtain the block emission factor. The module outputs emission factors for different blocks in different coal mines. The technical function of this module is to convert coal quality test parameters into fundamental factors that can participate in subsequent weighted calculations.

[0037] The inputs to the coal mine factor database construction module are block emission factors, block reserves, and block recovery rates. Since the coal quality may differ across blocks within the same coal mine, and the contributions of different blocks to the actual coal supply also vary, this module does not directly use a simple arithmetic average. Instead, it uses both block reserves and recovery rates to characterize the block contribution and weights the block emission factors accordingly. The module outputs coal mine-level coal-fired carbon emission factors and a coal-fired carbon emission factor database for coal-producing regions. The technical function of this module is to elevate block-level data to coal mine-level data, providing a stable data foundation for source matching on the enterprise side.

[0038] The input to the enterprise source matching module is the coal source data of the coal-using enterprise to be accounted for and the coal mine factor library. The coal source data includes the coal origin, coal mine enterprise name, and quantity of coal purchased. This module matches the coal origin and coal mine enterprise name to determine the corresponding record in the factor library for each type of coal source actually used by the coal-using enterprise. The output is the matching relationship between the enterprise's coal source and the records in the coal mine factor library. If no matching coal mine is found, the module outputs a source missing message, requiring the enterprise to supplement the coal mine source information or supplement the factor library record.

[0039] The input to the coal blending ratio processing module is the coal blending ledger of the coal-using enterprise and the amount of coal used from different coal mines. Its processing logic is to convert the amount of coal used from different coal mines into the usage ratio of the coal fed into the furnace, and to perform integrity and normalization checks on the ratio. The module outputs the usage ratio of coal from each coal mine. The technical function of this module is to transform the enterprise's actual coal blending structure into weights for localized factor calculation.

[0040] The input to the localization factor calculation module is the coal emission factor from each coal mine source determined by the enterprise source matching module and the usage ratio output by the coal blending ratio processing module. Its processing logic is to weight the emission factors of coal from different coal mine sources according to the usage ratio to obtain a localized coal-fired carbon emission factor adapted to the actual coal source and coal blending structure of the coal-using enterprise. The output is the localized coal-fired carbon emission factor. The technical function of this module is to realize the conversion from the regional coal mine factor database to the enterprise's coal input accounting factor.

[0041] The emissions accounting and reporting module takes as input the localized coal-fired carbon emission factor and the amount of coal used within the accounting period. Its processing logic involves multiplying the coal usage by the localized coal-fired carbon emission factor to obtain the coal-fired carbon emissions of the coal-using enterprise. The module outputs the coal-fired carbon emissions and optional data traceability information, including the source of the coal mine used, the coal blending ratio, the coal mine factor used, and the accounting period. The technical function of this module is to generate accounting results that can be directly used for enterprise carbon emission reporting or internal management.

[0042] Example 2: Introduction of Key Algorithm Units and Formulas

[0043] I. Block Emission Factor Determination Unit

[0044] In this technical scenario, major coal mines in coal-producing areas typically contain different sections. The coal quality of these different sections may vary, for example, in terms of lower heating value, carbon content, and carbon oxidation rate. If all sections of the same coal mine are simply considered to have the same coal quality, subsequent emission factors at the coal mine level would lose their basis in the differences between sections.

[0045] This technical challenge requires establishing comparable emission factors at the block level. Since the carbon emission factor needs to represent the amount of carbon dioxide emitted per unit of heat from coal combustion, it is necessary to combine the coal's carbon content with its carbon oxidation rate, convert this to a carbon dioxide emission contribution using the molecular weight conversion relationship between carbon and carbon, and then use the lower heating value as the heat baseline for calculation. (See attached reference.) Figure 3 Based on this processing logic, the segment emission factor can be calculated according to equation (1):

[0046] ;

[0047] The symbols in equation (1) are explained in the table below:

[0048]

[0049] The effect of Equation (1) is to convert the experimental test results of coal samples into a uniform dimension of emission factor for coal samples, so that different coal samples can be compared under the same unit heat benchmark, and to provide a basis for subsequent weighted calculations at the coal mine level.

[0050] II. Coal Mine Coal Emission Factor Database Establishment Unit

[0051] Even when the emission factor for a specific block is determined, it cannot be directly used to represent the entire coal mine. This is because different blocks within the same mine have different reserves, recovery rates, and therefore contribute differently to the actual coal supply. Simply using an arithmetic average might assign the same impact to blocks with smaller reserves or lower recoverability as to the main supply blocks, causing the mine-level factor to deviate from the actual supply characteristics.

[0052] To address the aforementioned technical problems, this invention uses both block reserves and block recovery rate to characterize the recoverable contribution of a block. Block reserves reflect the resource base of the block, while recovery rate reflects the degree to which the resources of the block can be actually extracted. The combination of the two is more suitable as a weight for the block emission factor. Based on this processing logic, the coal-fired carbon emission factor at the coal mine level can be calculated according to equation (2):

[0053] ;

[0054] The symbols in equation (2) are explained in the table below:

[0055]

[0056] By using Equation (2), the recoverable reserves weighted by the block reserves and recovery rate are used to weight the block emission factors, so that the coal-fired carbon emission factors at the coal mine level are closer to the emission characteristics of the coal actually supplied by the coal mine. Based on the coal-fired carbon emission factors of multiple major coal mines obtained according to Equation (2), a coal-fired carbon emission factor database of major coal mines in the coal-producing area can be formed.

[0057] In terms of database organization, the factor library should at least store the following information: coal mine enterprise name, coal origin, block identifier, lower heating value, carbon oxidation rate, carbon content, block reserves, block recovery rate, block emission factor, and coal mine coal combustion carbon emission factor. The setting of these fields is not a simple data stacking, but rather to ensure that subsequent coal sources for enterprises can be matched based on coal origin and coal mine enterprise name, while also ensuring that coal mine factors can be traced back to block test data and reserve recovery data. (See attached figure.) Figure 4 .

[0058] III. Unit for Determining Enterprise Coal Blending Ratio

[0059] On the coal-consuming enterprise side, the coal actually fed into the furnace is usually not from a single coal mine, but rather from multiple coal mines after blending. In this case, even if a coal mine factor database has been established, it is still impossible to obtain a localized factor suitable for the actual coal fed into the furnace of the enterprise without considering the blending ratio.

[0060] Reference Appendix Figure 5 The coal blending ratio can be determined based on the proportion of coal from different coal mines used in the coal blending process to the total amount of coal fed into the furnace. If the enterprise ledger directly records the percentage, it is necessary to confirm whether the sum of the proportions is one; if the enterprise ledger records the amount of coal used from different sources, it is necessary to convert the amount into a proportion. Based on this processing logic, the coal blending ratio can be determined according to formula (3).

[0061] ;

[0062] The symbols in equation (3) are explained in the table below:

[0063]

[0064] Equation (3) is used to convert the actual amount of coal used from different coal mines into a unified coal blending weight, so as to avoid the inability to directly calculate the localization factor due to inconsistent percentage records, different units, or the ledger only recording the amount of coal used.

[0065] IV. Localized Coal-fired Carbon Emission Factor Calculation Unit

[0066] After obtaining the coal mine factor library and the coal blending ratio of coal-using enterprises, it is necessary to convert the coal production-side factors into enterprise-side factors. This conversion cannot be based solely on the factors of a major coal mine, nor can it simply use regional averages, because the actual coal fed into the furnace by coal-using enterprises is a mixture of coal from different mines in a certain proportion.

[0067] Therefore, this invention retrieves coal-fired carbon emission factors from a coal-fired carbon emission factor library that match the coal sources of coal-using enterprises, and weights them according to the enterprise's coal blending ratio. The resulting factors inherit both the weighted results from the coal mine side and reflect the enterprise's coal blending structure. (See attached reference.) Figure 6 Based on this processing logic, the localized coal-fired carbon emission factor can be calculated according to equation (4):

[0068] ;

[0069] The symbols in equation (4) are explained in the table below:

[0070]

[0071] The technical advantage of Equation (4) is that it performs a secondary weighting of the factors of coal from different coal mines by the enterprise's coal blending ratio, forming a localized coal-fired carbon emission factor suitable for specific coal-using enterprises. Compared with the default value method, this factor can reflect the differences in coal source; compared with the simple prediction model, this factor directly utilizes the actual source and coal blending structure of the enterprise, and can better adapt to the random changes in the quality of coal entering the furnace.

[0072] V. Coal-fired Carbon Emission Calculation Unit

[0073] After obtaining the localized coal-fired carbon emission factor, the enterprise needs to combine it with its coal consumption during the accounting period to output the enterprise's coal-fired carbon emissions. Coal consumption can be expressed in calorific value, in gigajoules (GJ). This step corresponds to the enterprise's final accounting result output.

[0074] Since the unit of the localized coal-fired carbon emission factor is tons of carbon dioxide per gigajoule, and the unit of coal consumption is gigajoules, multiplying the two yields tons of carbon dioxide. Based on this processing logic, coal-fired carbon emissions can be calculated according to equation (5):

[0075] ;

[0076] The symbols in equation (5) are explained in the table below:

[0077]

[0078] Equation (5) can convert localization factors into carbon emissions within the enterprise's accounting cycle. Since the localization factors already include differences in coal mine blocks and enterprise coal blending, the final emissions can better reflect the enterprise's actual coal combustion structure.

[0079] Example 3: Abnormal Scene Handling and Calculation Stability

[0080] In practical applications, data sources may be incomplete or contain discrepancies in definitions. Without anomaly handling, the accounting program may output results that appear complete but are untraceable. Therefore, this embodiment verifies common anomalies in real-world scenarios without altering the core accounting formula, making the method more suitable for enterprise implementation.

[0081] Reference Appendix Figure 7 The first type of anomaly is a mismatch in coal source. This anomaly typically occurs when the name of the coal mine recorded in the coal-using enterprise's ledger does not match the record in the factor library, or when the coal mine has not yet been included in the coal-fired carbon emission factor library for the coal-producing region. The processing logic is as follows: first, perform an exact match based on the coal origin and the coal mine name; if the exact match fails, output a prompt for supplementary source information and request the addition of the coal mine name, coal origin, or factor library record. Before a reliable match is found, do not directly substitute other coal mine factors to avoid introducing unexplained bias.

[0082] The second type of anomaly is incomplete coal blending ratio. The enterprise ledger may record the purchase quantity of coal from different coal mines, but does not directly record the coal blending ratio for use in the furnace; it may also be due to different statistical methods that the sum of the ratios is not equal to one. The processing logic is as follows: when the record is the usage quantity, it is first converted into a ratio according to formula (3); when the record is a percentage and the sum of the ratios deviates from one, if the deviation is a small deviation caused by the input accuracy, it is normalized; if the deviation exceeds the preset management allowable range, a data review prompt is output. This processing can avoid the localization factor calculation being affected by different ratio recording methods.

[0083] The third type of anomaly is invalid block reserves or recovery rates. If a block's reserves are zero, its recovery rate is empty, or its recovery rate is outside a reasonable range, then that block cannot be used as a valid weight in the coal mine factor calculation. The handling logic is as follows: mark the block and prompt for supplementation of annual reserve reports or recovery rate data; before supplementation, the block will not participate in the coal mine factor library update, or the data already verified in the previous version will be retained. This handling can prevent invalid weights from causing distortion of coal mine factors.

[0084] The fourth type of anomaly is missing test parameters. If a coal sample from a certain segment lacks lower heating value, carbon oxidation rate, or carbon content, the emission factor for that segment cannot be determined. The processing logic is as follows: mark the segment as pending testing and prompt for supplementary sample test data. For other segments with complete test data, their segment emission factors and coal mine factors can continue to be calculated, but the missing segment status is retained in the factor library for future updates.

[0085] The fifth anomaly is data lag in the factor database. When changes occur in the coal mine's production scope, annual reserve reports, recovery rate, or major coal supply blocks, the cause sub-database may not accurately reflect the current coal mine supply characteristics. The handling logic is as follows: set update trigger conditions; once coal petrology and quality data, production status, reserves, or recovery rate change, the corresponding coal mine records are updated; if the coal-using enterprise's coal blending structure changes, only the enterprise's localized coal-fired carbon emission factors are updated, without needing to rebuild the entire regional factor database.

[0086] Through the aforementioned anomaly handling, this invention does not simply output calculation results, but rather forms a traceable, verifiable, and updatable accounting chain. Anomaly handling does not alter the core two-level weighted path of this invention; rather, it ensures consistency between input data and accounting logic, enabling enterprises to clearly identify necessary supplements or reviews under varying data quality conditions.

[0087] Example 4: Data Update, Security Management, and Traceability Implementation

[0088] This embodiment illustrates the data update, security management, and traceability methods when implementing the present invention in a computer system. This part does not change the accounting principle, but rather aims to ensure that the coal mine factor database and enterprise accounting results can be used long-term in actual management.

[0089] Regarding data updates, the system can assign version numbers to the coal mine factor library. Each version of the factor library corresponds to a set of basic coal mine data, block test data, reserves, and recovery rate data. When coal production enterprises update their annual reserve reports, coal mine production blocks change, or new sample test results are added, the system generates a new version of the factor library while retaining the old version. In this way, when calculating emissions for different years or different accounting periods, the version of the factor library used can be clearly identified, avoiding the impact of subsequent data updates on the traceability of historical accounting results.

[0090] Regarding the updating of enterprise localization factors, the system can link and save an enterprise's coal source, coal blending ratio, and accounting cycle. If an enterprise only changes its coal blending ratio while the coal mine factor database remains unchanged, the system only needs to recalculate the enterprise's localized coal-fired carbon emission factor. If the coal mine factor database changes, the system can prompt the relevant enterprise to rematch its coal source and recalculate the localization factor. This process reduces unnecessary duplication of database construction and improves the efficiency of practical applications.

[0091] Regarding data security, coal mine coal quality data, annual reserve report data, and enterprise coal blending data may involve enterprise operational data. The system can differentiate data entry, data review, and result viewing permissions through user permission management. Modification operations to the coal mine factor library can be performed only by users with review permissions; enterprise coal blending data can be viewed only by the enterprise or authorized verification personnel; when outputting accounting reports, only necessary factors, proportions, and results can be displayed, without exposing unnecessary original operational information.

[0092] In terms of data traceability, each accounting result can save the list of source coal mines, corresponding coal mine factors, coal blending ratio, coal consumption, accounting time, and the version of the factor library used. Through this traceability information, third-party auditors can trace back from the final emissions to the company's localized factors, and then back to the coal mine factor library and block data, thereby improving the interpretability of the accounting results.

[0093] The aforementioned data updates, security management, and traceability all serve the core accounting method of this invention. Its technical function is to ensure that the coal-fired carbon emission factor database can not only be used once, but also adapt to updates to coal-producing region data and changes in coal blending by coal-consuming enterprises, while maintaining the stability and verifiability of results in multi-period accounting.

[0094] Example 5: Implementation of Electronic Devices

[0095] The electronic device of this invention includes a processor, a memory, and a data interface. The memory stores a coal petrology and quality dataset, a coal-fired carbon emission factor library, enterprise coal source data, coal blending data, and a calculation program. When the processor executes the calculation program, it sequentially completes data acquisition, sample test data processing, block factor determination, coal mine factor library establishment, enterprise source matching, coal blending ratio processing, localized factor calculation, and emission calculation.

[0096] The data interface can be used to import annual reports on coal production enterprise reserves, coal sample test results, enterprise procurement records, coal blending ledgers, and coal consumption data. The data interface can also be used to export accounting reports. For data that cannot be directly imported through the interface, the system provides a manual data entry interface and performs field integrity and unit consistency checks after data entry.

[0097] The coal-fired carbon emission factor database in memory can be stored in the form of structured tables. Coal mine-level records and block-level records are linked through the coal mining company name, coal origin, and block identifier; enterprise-level records and coal mine-level records are linked through the coal origin and coal mining company name. This association structure enables the system to locate coal mine factors from the enterprise's coal source, and then trace back from the coal mine factors to block test data and reserve recovery data.

[0098] In terms of output, the computer equipment can generate localized coal-fired carbon emission factors, coal-fired carbon emission figures, and accounting traceability tables. The accounting traceability tables can list the coal mine sources involved in the calculation, the corresponding coal mine factors, coal blending ratios, and coal consumption. This output not only provides the final values ​​but also explains the source of those values, facilitating internal management and third-party verification.

[0099] The implementation methods and steps of the electronic device in this embodiment correspond one-to-one. In actual deployment, the electronic device can be a local server, an enterprise intranet server, or a standalone computing device. As long as its execution of the accounting logic falls within the above method flow, it belongs to the implementation of this invention.

[0100] Example 6: Carbon Emission Calculation of a Coal-fired Power Plant in Datong, Shanxi Province

[0101] This embodiment uses a coal-fired power plant in Datong, Shanxi Province as an example to illustrate the application process of the present invention. First, based on the production situation, coal quality, and test data of major coal mines near Datong, Shanxi Province, a coal-fired carbon emission factor database for coal mining enterprises in Shanxi Province is constructed. The establishment of this factor database follows the aforementioned steps: data collection and sample testing are conducted on different blocks of major coal mines to determine the block emission factors, and then the coal-fired carbon emission factors are obtained by weighting the block reserves and recovery rate.

[0102] Subsequently, the coal sources and blending of the coal-fired power plant were analyzed. The results showed that the power plant primarily used coal from eight coal production enterprises, including Datong Coal Mine and Datong Coal Industry. For each coal mine, the corresponding coal emission factor could be matched in a factor library based on the coal origin and the name of the coal mining enterprise. The proportion of coal blended into the furnace was determined based on the proportion of coal from different mines used in the power plant's blending process.

[0103] In this embodiment, the emission factors and blending ratios of coal from certain coal mines can be represented in the table below. This table illustrates the enterprise source matching and coal blending weighting process; the specific coal mine names can be determined based on enterprise data and factor database records.

[0104]

[0105] Based on the emission factors from the aforementioned coal mines and their blending ratios, the localized coal-fired carbon emission factor of the enterprise after blending is calculated according to formula (4), and the result is 0.0945059 tons of carbon dioxide per gigajoule. This result has already taken into account the proportion of coal from different coal mines in the coal fed into the furnace, and therefore differs from the single coal mine factor and the unified default factor.

[0106] According to the survey results, the annual coal consumption of this coal-fired power plant is approximately 3.6 × 10^7 gigajoules. Substituting this coal consumption and the localized coal-fired carbon emission factor into equation (5), the enterprise's coal-fired carbon emissions are calculated to be 3.4438 million tons of carbon dioxide. The carbon emissions of this power plant calculated according to the national standard "Greenhouse Gas Emissions Accounting and Reporting Requirements Part 1: Power Generation Enterprises" are 3.3125 million tons of carbon dioxide, with an error of 3.9%.

[0107] To represent the difference between the two accounting results in the embodiments, the error ratio can be calculated using equation (6). This formula is only used for comparing the results of the embodiments and does not change the core accounting path of the present invention.

[0108] ;

[0109] The symbols in equation (6) are explained in the table below:

[0110]

[0111] This embodiment demonstrates that the present invention can complete localized accounting using a database of major coal mine factors in the coal-producing region and the enterprise's coal blending ratio, without requiring enterprises to collect coal samples daily and conduct continuous high-frequency testing. By combining coal mine block weighting and enterprise coal blending weighting, the method can maintain high accounting accuracy while reducing accounting costs.

[0112] Example 7: Verification of Calculation Results and Output of Reports

[0113] To ensure the accounting results meet the needs of internal enterprise review and third-party verification, this embodiment conducts a three-tiered review after calculating coal-fired carbon emissions. The first tier is the input data review, checking whether the coal mine's basic data, sample test data, block reserves, block recovery rate, enterprise coal sources, coal blending ratio, and coal consumption all have source records. If a certain input data lacks a source record, the data is marked as an item to be reviewed in the report and is not directly included in the final confirmation as data without a source.

[0114] The second layer of verification is the calculation chain verification, which checks whether the connection between the block emission factor, the coal mine coal-fired carbon emission factor, the localized coal-fired carbon emission factor of the coal-using enterprise, and the final coal-fired carbon emission is complete. Specifically, the system needs to be able to trace back from the final emission to the coal consumption and localized factors, then from the localized factors to the factors and blending ratios of the coal sourced from each coal mine, and further from the coal mine factors to the block emission factor, reserves, and recovery rate. This verification avoids the problem of only providing the final value without explaining the data source.

[0115] The third layer of verification is the result rationality verification, which checks whether the localized coal-fired carbon emission factor deviates significantly from the range of factors of the coal mines participating in coal blending in the factor library, whether there are any abnormal abrupt changes in the coal blending ratio, and whether the coal consumption is consistent with the accounting cycle. When the localized factor exceeds the maximum and minimum value range of the factors of the coal mines participating in coal blending, it indicates that there may be errors in matching, ratio, or unit, and the system outputs a verification prompt. This process does not introduce a new prediction model, but rather uses the existing factor library and coal blending relationships of this invention to perform consistency checks.

[0116] The accounting report may include the accounting period, the name of the coal-using enterprise, the source of the coal mines involved in coal blending, the proportion of coal used from each mine, the carbon emission factor of each coal mine, the localized carbon emission factor, the amount of coal used, the amount of carbon emissions, and the version number of the factor library. Through this report structure, the accounting results not only include emission figures but also the key data chains supporting those figures. This facilitates understanding that the invention is not an abstract data calculation rule, but rather an implementable data processing method centered around the localized accounting scenario of coal-fired enterprises.

[0117] The difference between this invention and existing technologies lies not in proposing the emission factor method itself, but in proposing a factor acquisition and conversion path suitable for localized accounting of coal-fired enterprises. This path includes determining the emission factors for specific blocks, weighting based on recoverable coal reserves, matching coal sources for enterprises, secondary weighting based on coal blending ratios, and outputting coal-fired carbon emissions.

[0118] Compared to solutions that rely on daily coal input testing parameters and continuous flue gas monitoring data, this invention does not require daily testing and continuous monitoring as necessary conditions. Instead, it reduces the need for high-frequency testing at the enterprise level by utilizing a database of key coal mine factors in the coal-producing region. This difference addresses the issue of high testing costs in practical enterprise implementation.

[0119] Compared to schemes based on coal type classification models, carbon content prediction models, lower heating value prediction models, or unit operation data models, this invention does not use model predictions as the primary source of factors. Instead, it establishes coal mine factors based on coal mine block sample testing and reserve recovery weights, and then combines these with the actual coal blending ratio of the enterprise to form localized factors. This difference can reduce the impact of random variations in the quality of coal fed into the furnace on fixed prediction models.

[0120] Compared to schemes that directly adopt standard default values, this invention forms a hierarchical accounting chain through coal mine block data and enterprise coal blending data, which can more accurately reflect the differences between the coal producing and consuming ends. In particular, when the same coal-consuming enterprise has coal from multiple coal mines mixed into the furnace, the enterprise-side secondary weighting of this invention can make the accounting factors change with the coal blending structure.

[0121] In summary, this invention establishes a coal mine-level coal-fired carbon emission factor library based on emission factors, reserves, and recovery rates of different coal mine sections. Then, it converts the coal mine factors in the factor library into enterprise-localized factors based on the coal source and blending ratio of the coal-using enterprise, ultimately completing the calculation of coal-fired carbon emissions. This combination is not a simple listing of existing emission factor methods, but rather a continuous processing chain formed around the technical issues of "low cost, reflecting coal quality differences, and adapting to the randomness of coal blending."

[0122] In one alternative implementation, the coal-producing region can be determined based on the main procurement areas of the coal-consuming enterprises. If a coal-consuming enterprise mainly purchases coal from mines near Datong, Shanxi, the factor database can prioritize covering the main coal mines in that region; if the enterprise's procurement area changes, the coverage of the factor database can be expanded. This implementation method allows the workload of factor database construction to match the actual coal sources of the enterprises.

[0123] In another alternative implementation, coal source analysis can prioritize enterprise purchase contracts, coal receiving and acceptance records, warehousing records, and coal blending ledgers. If the coal mining enterprise names in the above data contain abbreviations or aliases, an alias field can be set in the factor library or the matching relationships can be manually reviewed. This process does not change the accounting logic but improves the operability of enterprise source matching.

[0124] In another alternative implementation, the coal blending ratio can be determined separately according to the accounting period. For example, enterprises can calculate the usage ratio of coal from different coal mines on a monthly, quarterly, or annual basis, and calculate the localized coal-fired carbon emission factor for the corresponding period. If the annual coal blending structure changes significantly, the calculations can be performed separately for shorter periods first, and then the annual emissions can be obtained by summing them up. This implementation method can adapt to changes in the enterprise's coal blending structure over time.

[0125] In another alternative implementation, coal consumption can be expressed in calorific value. If the company's original records are in terms of coal quality, they can first be converted to a calorific value based on the corresponding lower heating value, and then combined with the localized coal carbon emission factor. This conversion is part of the unified processing of activity-level data, aiming to ensure that the unit of coal consumption corresponds to the unit of the localized coal carbon emission factor.

[0126] In another alternative implementation, the accounting report may include the final coal-fired carbon emissions, localized coal-fired carbon emission factors, the source of the coal mines involved in the calculation, the corresponding coal blending ratios, and the version of the factor library. This report structure allows companies not only to obtain the final results but also to explain the source of those results, facilitating subsequent verification.

Claims

1. A localized method for accurate accounting of carbon emissions from coal combustion, characterized in that, include: S1. Data acquisition steps: Acquire regional coal mine basic data of major coal mines in the coal-producing area determined according to preset screening criteria, and collect coal samples from different blocks of the major coal mines. S2. Sample testing steps: Test the lower heating value, carbon oxidation rate and carbon content of each coal sample, and associate the test results with the regional coal mine basic data of the corresponding block to form a coal rock and coal quality dataset. S3. Block factor determination steps: Determine the corresponding block emission factor based on the lower heating value, carbon oxidation rate and carbon content of each block. S4. Steps for establishing a coal mine factor database: Based on the reserves and recovery rate of each block, the emission factors of the same coal mine block are weighted to obtain the coal mine coal combustion carbon emission factors, and a coal combustion carbon emission factor database for the coal-producing area is established. S5. Enterprise Source Matching Step: Obtain coal source data of the coal-using enterprises to be accounted for, and match the coal mine carbon emission factors in the factor library according to the coal production location and coal mine enterprise name. S6. Coal blending ratio processing steps: Obtain the amount of coal used by the coal-using enterprise in terms of calorific value within the accounting period, as well as the proportion of coal used from different coal mines during the coal blending process; S7. Localization factor calculation steps: Weight the matched coal mine carbon emission factor according to the usage ratio to obtain the localized coal emission factor of the coal-using enterprise. S8. Emission calculation steps: Multiply the coal consumption by the localized coal carbon emission factor to obtain the coal carbon emissions of the coal-using enterprise; In the block factor determination step, the carbon content, carbon oxidation rate and molecular weight conversion relationship of the block coal are combined, and the lower heating value of the block coal is used as the unit heat conversion benchmark to obtain the block emission factor corresponding to the unit heat of coal combustion. In the coal mine factor database establishment step, the product of the reserves and recovery rate of each block within the same coal mine is used as the recoverable reserve weight of the corresponding block, and the emission factors of each block are weighted and averaged using the recoverable reserve weight to obtain the coal-fired carbon emission factors of the main coal mine.

2. The method for accurate localization of coal-fired carbon emission calculation according to claim 1, characterized in that, The regional coal mine basic data includes coal production location, coal mining enterprise name, coal rock and coal quality data of different blocks, reserves of different blocks, recovery rate of different blocks, and coal mine production status; the coal samples of different blocks are used to correlate with the coal rock and coal quality data of the corresponding blocks; the preset screening criteria are: sorted from high to low according to the coal purchase volume or coal input volume of the coal-using enterprises to be accounted for in the accounting period, and coal mines whose cumulative coal supply accounts for more than 80% of the total coal input volume of the coal-using enterprises to be accounted for in the accounting period are selected as the main coal mines in the coal-producing area.

3. The method for accurate localization of coal-fired carbon emission calculation according to claim 1, characterized in that, The coal-producing area carbon emission factor database includes at least the coal mining enterprise name, coal origin, block identifier, lower heating value, carbon oxidation rate, carbon content, block reserves, block recovery rate, block emission factor, and coal mine carbon emission factor; the enterprise source matching step uses the coal origin and coal mining enterprise name as matching fields to determine the coal carbon emission factor of the corresponding coal mine source.

4. The method for accurate localization of coal-fired carbon emission calculation according to claim 1, characterized in that, In the coal blending ratio processing step, the usage ratio is determined based on the proportion of coal from different coal mines used in the coal blending process to the total amount of coal fed into the furnace; when the sum of the usage ratios of coal from different coal mines is not equal to one, the usage ratio is normalized before being used for localization factor calculation.

5. The method for accurate localization of coal-fired carbon emission calculation according to claim 1, characterized in that, After the enterprise source matching step and the coal blending ratio processing step, an anomaly verification step is also included. The anomaly verification step includes verifying whether the coal mine source is matched, whether the coal blending ratio is complete, whether the block reserves are greater than 0, whether the recovery rate is greater than 0 and not greater than 100%, whether the lower heating value is greater than 0, whether the carbon oxidation rate is greater than 0 and not greater than 100%, and whether the carbon content is greater than 0. If the verification fails, the step will output a supplementary data prompt, remove invalid data, or trigger an update of the factor library.

6. The method for accurate localization of coal-fired carbon emission calculation according to claim 1, characterized in that, When changes occur in the coal rock and coal quality data, production status, reserves of different blocks, recovery rate of different blocks, or coal blending of coal-consuming enterprises to be accounted for in the major coal mines of the coal-producing area, the coal-fired carbon emission factor database of the coal-producing area or the localized coal-fired carbon emission factor of the coal-consuming enterprises to be accounted for shall be updated.

7. A localized, precise carbon emission accounting system for coal combustion, characterized in that, include: The system comprises a regional coal mine data acquisition module, a sample testing and data processing module, a block factor determination module, a coal mine factor library establishment module, an enterprise source matching module, a coal blending ratio processing module, a localized factor calculation module, and an emission accounting module; wherein each module is configured to perform the method described in any one of claims 1 to 6.

8. An electronic device, characterized in that, It includes a processor, a memory, and a program stored in the memory and executable by the processor, wherein the program, when executed by the processor, implements the localized coal-fired carbon emission accurate accounting method according to any one of claims 1 to 6.

Images