Thermal power generation carbon emission monitoring system and method

Abstract

The invention discloses a thermal power generation carbon emission monitoring system and method, and relates to the technical field of thermal power generation, and the system comprises a data collection layer, a data processing layer, a data application layer, and a credibility verification and self-diagnosis module. The data acquisition layer is deployed in a thermal power plant site and comprises a fuel metering unit, a fuel component analysis unit, a flue gas continuous emission monitoring unit and a DCS / SCADA interface; the data processing layer comprises a data preprocessing module, a core calculation module and a block chain evidence storage module; the data processing layer is in communication connection with the data acquisition layer; the data application layer is in communication connection with the data processing layer, and the data application layer comprises a visual display module, a data storage and management module, a report generation module and a remote access interface; and the credibility verification and self-diagnosis module is connected with the data processing layer and the data acquisition layer. According to the thermal power generation carbon emission monitoring system and method, the monitoring precision is improved through multi-source data fusion, and the operation and maintenance cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of thermal power generation technology, and in particular to a system and method for monitoring carbon emissions from thermal power generation. Background Technology

[0002] Single-path monitoring has large errors, and fuel-end monitoring relies on offline coal quality testing (2-4 times / day), which cannot reflect fuel characteristic fluctuations in real time. The estimation deviation of key parameters is significant: belt scale drift (±1.5%), carbon oxidation rate (η) fixed value (actual fluctuation ±3%), with cumulative error exceeding ±15%.

[0003] Flue gas end monitoring methods are limited by sensor accuracy: flue gas flow, temperature, and pressure compensation distortion (±5%), CO2 concentration interference from moisture, frequent probe blockage / calibration misalignment, resulting in a comprehensive error of ±8%, and complete data interruption during faults. Data silos and collaborative failures exist, high accuracy relies on manual intervention (such as coal quality analysis), but real-time requirements force simplified models (fixed η value), leading to monitoring results deviating from reality by more than ±10%; existing systems directly interrupt data flow when equipment fails, lacking an adaptive compensation mechanism. Summary of the Invention

[0004] The purpose of this invention is to provide a carbon emission monitoring system and method for thermal power generation, which improves monitoring accuracy and reduces operation and maintenance costs through multi-source data fusion.

[0005] This invention provides a carbon emission monitoring system and method for thermal power generation, comprising a data acquisition layer, a data processing layer, a data application layer, and a reliability verification and self-diagnosis module. The data acquisition layer is deployed at the thermal power plant site and includes a fuel metering unit, a fuel composition analysis unit, a continuous emission monitoring unit for flue gas, and a DCS / SCADA interface. The data processing layer includes a data preprocessing module, a core computing module, and a blockchain storage module. The data processing layer is communicatively connected to the data acquisition layer. The data application layer is also communicatively connected to the data processing layer and includes a visualization module, a data storage and management module, a report generation module, and a remote access interface. The reliability verification and self-diagnosis module is connected to the data processing layer and the data acquisition layer.

[0006] Preferably, the fuel composition analysis unit includes an online coal quality analyzer, an offline test result database interface, or a fuel characteristic prediction model.

[0007] Preferably, the core computing module calculates the total carbon emissions based on the fuel end.

[0008] Preferably, the blockchain evidence storage module stores the hash values ​​of the following data into the blockchain: original fuel flow rate, original coal composition analysis results, original flue gas CO2 concentration, original flue gas flow rate, calculated fuel-end carbon emissions, calculated flue gas-end carbon emissions, fused carbon emissions, timestamp, and equipment status identifier.

[0009] Preferably, the preset rules of the credibility verification and self-diagnosis module include a mapping table of the association between equipment status and data deviation, a normal range threshold for historical data intervals, a material or energy conservation relationship between key parameters, and a mapping relationship between equipment diagnostic status and data credibility.

[0010] Preferably, the data application layer includes a data security module.

[0011] Preferably, a method for monitoring carbon emissions from a thermal power plant includes the following steps: Step S1: Real-time acquisition of the amount of fuel fed into the furnace, the composition and lower heating value of the fuel fed into the furnace, the CO2 concentration / O2 concentration / flow rate / temperature / pressure of the flue gas, and the power generation / heat supply of the unit through the data acquisition layer; Step S2: Preprocess the data collected in step S1 at the data processing layer; Step S3: In the data processing layer, the theoretical carbon emissions based on the fuel end and the theoretical carbon emissions based on the flue gas end are calculated independently based on the preprocessed data. Step S4: At the data processing layer, based on the credibility verification rules and the real-time device diagnostic status, determine the real-time credibility of the fuel end data and the flue gas end data. Step S5: In the data processing layer, based on the real-time credibility determined in step S4, the two theoretical carbon emissions calculated in step S3 are weighted and fused to calculate the credibility, and the final approved real-time carbon emissions, fuel-end carbon emissions and flue gas-end carbon emissions are output. Step S6: In the data processing layer, the key raw data fingerprint of step S1, the key processing parameter fingerprint of steps S3 / S5, and the final approved carbon emissions of step S5 are packaged and uploaded to the blockchain for evidence storage. Step S7: Send the final approved carbon emissions and other relevant data output in step S5 to the data application layer for real-time display, storage management, report generation and reporting. Step S8: Continuously run the credibility verification and self-diagnosis process: monitor the abnormal fluctuations of the status of the acquisition device and key calculation parameters, evaluate the credibility of the data and the rationality of the results based on preset rules, and output the verification results and necessary alarm information.

[0012] Therefore, the present invention adopts the above-mentioned carbon emission monitoring system and method for thermal power generation, which improves monitoring accuracy and reduces operation and maintenance costs through multi-source data fusion.

[0013] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the overall structure of a carbon emission monitoring system for thermal power generation according to the present invention; Figure 2 This is a schematic diagram of the data acquisition layer in a carbon emission monitoring system for thermal power generation according to the present invention; Figure 3 This is a schematic diagram of the data processing layer in a carbon emission monitoring system for thermal power generation according to the present invention; Figure 4 This is a schematic diagram of the data application layer in a carbon emission monitoring system for thermal power generation according to the present invention; Figure 5 This is a schematic diagram of the overall process of a method for monitoring carbon emissions from thermal power generation according to the present invention. Detailed Implementation

[0015] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0016] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0017] The terms "first," "second," and similar words used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] Example 1 like Figures 1-5As shown, this invention discloses a carbon emission monitoring system and method for thermal power generation, comprising a data acquisition layer, a data processing layer, a data application layer, and a reliability verification and self-diagnosis module. The data acquisition layer is deployed at the thermal power plant site and includes a fuel metering unit, a fuel composition analysis unit, a continuous emission monitoring unit for flue gas, and a DCS / SCADA interface. The fuel metering unit is used to acquire the amount of fuel fed into the furnace in real time; the fuel composition analysis unit is used to acquire the composition and lower heating value of the fuel fed into the furnace in real time; the continuous emission monitoring unit for flue gas is used to acquire the CO2 concentration, O2 concentration, flue gas flow rate, flue gas temperature, and pressure in the flue gas in real time; and the DCS / SCADA interface is used to acquire the unit's power generation or heating output.

[0019] The fuel composition analysis unit includes an online coal quality analyzer, an offline test result database interface, or a fuel characteristic prediction model.

[0020] The data processing layer includes a data preprocessing module, a core computing module, and a blockchain notarization module. The data processing layer communicates with the data acquisition layer. The data preprocessing module cleans, formats, and aligns the acquired data. The core computing module calculates total carbon emissions based on fuel consumption and carbon content, and calculates total carbon emissions based on flue gas flow and CO2 concentration. It then performs a weighted reliability fusion of the discrepancies between the two calculations, outputting real-time carbon emissions, fuel-based carbon emissions, and flue gas-based carbon emissions. The blockchain notarization module packages and uploads key raw data fingerprints, core computing parameter fingerprints, and the finally approved carbon emission data to the blockchain network for notarization.

[0021] The core calculation module calculates the total carbon emissions based on the fuel end; it uses the amount of fuel fed into the furnace, the carbon content or carbon oxidation rate of the fuel fed into the furnace, and the lower heating value of the fuel fed into the furnace, combined with the carbon emission factor at the fuel end or a direct calculation method based on chemical equilibrium to perform the calculation.

[0022] The blockchain evidence storage module stores the hash value of at least one of the following data into the blockchain: original fuel flow rate, original coal composition analysis results, original flue gas CO2 concentration, original flue gas flow rate, calculated fuel-end carbon emissions, calculated flue gas-end carbon emissions, fused carbon emissions, timestamp, and equipment status identifier.

[0023] The core calculation module calculates the total carbon emissions based on the flue gas end in the following way: using the flue gas flow rate, CO2 concentration, O2 concentration, flue gas temperature and pressure, combined with dry / wet basis conversion, state equation and flow rate standard state conversion rules, it calculates the standardized flue gas CO2 emissions, and calculates the total CO2 emissions based on the flue gas flow rate.

[0024] The data application layer communicates with the data processing layer. The data application layer includes a visualization module, a data storage and management module, a report generation module, and a remote access interface. The visualization module displays carbon emissions, emission intensity, and key parameters in real time. The data storage and management module stores raw data, processing data, and final results. The report generation module automatically generates carbon emission reports that meet regulatory requirements. The remote access interface provides data to higher-level regulatory platforms or carbon trading platforms.

[0025] The data application layer includes a data security module, which is used to encrypt and control access to, transmission and storage of system data to ensure compliance with relevant data security management standards.

[0026] The reliability verification and self-diagnosis module is connected to the data processing layer and the data acquisition layer. It receives equipment status diagnostic information from the data acquisition layer. It also receives the carbon emission deviation values ​​between the fuel end and the flue gas end, as well as intermediate variables from the fusion algorithm, from the data processing layer. Based on preset rules and real-time data, it evaluates the reliability of each measurement data point from the data acquisition layer and verifies the rationality of the output of the core calculation module, outputting the verification results and alarm information.

[0027] Based on the credibility of fuel-side data and flue gas-side data output by the credibility verification and self-diagnosis module, weights are assigned to the total carbon emissions based on the fuel side and the total carbon emissions based on the flue gas side, respectively. Weighted average or adaptive filtering algorithm is used for fusion calculation to output the optimal real-time carbon emissions.

[0028] The preset rules of the credibility verification and self-diagnosis module include a mapping table of the association between equipment status and data deviation, the normal range threshold of historical data intervals, the material or energy conservation relationship between key parameters, and the mapping relationship between equipment diagnostic status and data credibility.

[0029] A method for monitoring carbon emissions from thermal power generation includes the following steps: Step S1: Real-time acquisition of the amount of fuel entering the furnace, the composition and lower heating value of the fuel entering the furnace, the CO2 concentration / O2 concentration / flow rate / temperature / pressure of the flue gas, and the power generation / heat supply of the unit through the data acquisition layer.

[0030] Step S2: Preprocess the data collected in step S1 at the data processing layer; Step S3: In the data processing layer, the theoretical carbon emissions based on the fuel end and the theoretical carbon emissions based on the flue gas end are calculated independently based on the preprocessed data.

[0031] The theoretical carbon emissions EFuel from the fuel end are shown in the following formula: ; in, This refers to the mass flow rate of the fuel entering the furnace. This represents the percentage of carbon by mass in the fuel. 44 is the molecular weight conversion factor from carbon to carbon dioxide, 12 is the molecular weight of CO2, and 44 is the atomic weight of carbon. This represents the carbon oxidation rate of fuel combustion, ranging from 0 to 1 (dimensionless), with a default value of 0.98.

[0032] Dynamic calculation, as shown in the following formula: ; Theoretical carbon emissions EExhaust from flue gas: converted from baseline oxygen content, as shown in the following formula: ; in, This represents the measured volume concentration of CO2 in the flue gas. The standard oxygen content as specified by national regulations; This represents the measured volume concentration of O2 in the flue gas. This is the CO2 concentration converted to a baseline oxygen content.

[0033] The standard dry flue gas flow rate is calculated as follows: ; in, To measure the actual flue gas flow rate under operating conditions; The flue gas temperature; Standard atmospheric pressure = 101.325 kPa; The absolute pressure of the flue gas; The dry flue gas flow rate is under standard conditions (0℃, 101.325kPa).

[0034] CO2 mass emissions are calculated as shown in the following formula: ; in, This is the density coefficient of CO2 under standard conditions.

[0035] Step S4: In the data processing layer, based on the credibility verification rules and the real-time equipment diagnostic status, determine the real-time credibility based on fuel end data and flue gas end data.

[0036] The real-time device diagnostic status is shown in the following formula: ; in, The overall health index of the equipment group (range: 0~1); The status level coefficient of the i-th device (range: 0~1).

[0037] Data quality diagnostics are shown in the following formula: ; in, This is the data fluctuation suppression coefficient (range: 0~1). This is the standard deviation of the parameter within the sliding window (10 sampling points); This is the maximum permissible fluctuation threshold.

[0038] Cross-validation verification: The real-time reliability of fuel-side data in carbon mass conservation verification is shown in the following formula: ; in, This is the comprehensive condition coefficient for fuel-side equipment; This is the fuel data fluctuation suppression coefficient; This represents the consistency coefficient between the two source data.

[0039] The real-time reliability of flue gas end data is shown in the following formula: ; in, This is the comprehensive condition coefficient for the flue gas side equipment; This is the flue gas data fluctuation suppression coefficient; This represents the consistency coefficient between the two source data.

[0040] Step S5: In the data processing layer, based on the real-time credibility determined in step S4, the two theoretical carbon emissions calculated in step S3 are weighted and fused to calculate the credibility, and the final approved real-time carbon emissions, fuel-end carbon emissions, and flue gas-end carbon emissions are output.

[0041] Step S6: In the data processing layer, the key raw data fingerprint of step S1, the key processing parameter fingerprints of steps S3 / S5, and the final approved carbon emissions of step S5 are packaged and uploaded to the blockchain for evidence storage.

[0042] Step S7: Send the final approved carbon emissions and other relevant data output in step S5 to the data application layer for real-time display, storage management, report generation and reporting.

[0043] Step S8: Continuously run the credibility verification and self-diagnosis process: monitor the abnormal fluctuations of the status of the acquisition device and key calculation parameters, evaluate the credibility of the data and the rationality of the results based on preset rules, and output the verification results and necessary alarm information.

[0044] Therefore, the present invention adopts the above-mentioned carbon emission monitoring system and method for thermal power generation, which improves monitoring accuracy and reduces operation and maintenance costs through multi-source data fusion.

[0045] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A carbon emission monitoring system for thermal power generation, characterized in that, It includes a data acquisition layer, a data processing layer, a data application layer, and a credibility verification and self-diagnosis module. The data acquisition layer is deployed at the thermal power plant site and includes a fuel metering unit, a fuel composition analysis unit, a continuous emission monitoring unit for flue gas, and a DCS / SCADA interface. The data processing layer includes a data preprocessing module, a core computing module, and a blockchain evidence storage module. The data processing layer communicates with the data acquisition layer. The data application layer also communicates with the data processing layer and includes a visualization module, a data storage and management module, a report generation module, and a remote access interface. The credibility verification and self-diagnosis module is connected to the data processing layer and the data acquisition layer.

2. The carbon emission monitoring system for thermal power generation according to claim 1, characterized in that, The fuel composition analysis unit includes an online coal quality analyzer, an offline test result database interface, or a fuel characteristic prediction model.

3. The carbon emission monitoring system for thermal power generation according to claim 1, characterized in that, The core computing module calculates total carbon emissions based on the fuel end.

4. The carbon emission monitoring system for thermal power generation according to claim 1, characterized in that, The blockchain evidence storage module stores the hash values ​​of the following data into the blockchain: original fuel flow rate, original coal composition analysis results, original flue gas CO2 concentration, original flue gas flow rate, calculated fuel-end carbon emissions, calculated flue gas-end carbon emissions, merged carbon emissions, timestamp, and equipment status identifier.

5. A carbon emission monitoring system for thermal power generation according to claim 1, characterized in that, The preset rules of the credibility verification and self-diagnosis module include a mapping table of the association between equipment status and data deviation, the normal range threshold of historical data intervals, the material or energy conservation relationship between key parameters, and the mapping relationship between equipment diagnostic status and data credibility.

6. The carbon emission monitoring system for thermal power generation according to claim 1, characterized in that, The data application layer includes a data security module.

7. The method for monitoring carbon emissions from thermal power generation as described in any one of claims 1-6, characterized in that, Includes the following steps: Step S1: Real-time acquisition of the amount of fuel fed into the furnace, the composition and lower heating value of the fuel fed into the furnace, the CO2 concentration / O2 concentration / flow rate / temperature / pressure of the flue gas, and the power generation / heat supply of the unit through the data acquisition layer; Step S2: Preprocess the data collected in step S1 at the data processing layer; Step S3: In the data processing layer, the theoretical carbon emissions based on the fuel end and the theoretical carbon emissions based on the flue gas end are calculated independently based on the preprocessed data. Step S4: At the data processing layer, based on the credibility verification rules and the real-time device diagnostic status, determine the real-time credibility of the fuel end data and the flue gas end data. Step S5: In the data processing layer, based on the real-time credibility determined in step S4, the two theoretical carbon emissions calculated in step S3 are weighted and fused to calculate the credibility, and the final approved real-time carbon emissions, fuel-end carbon emissions and flue gas-end carbon emissions are output. Step S6: In the data processing layer, the key raw data fingerprint of step S1, the key processing parameter fingerprint of steps S3 / S5, and the final approved carbon emissions of step S5 are packaged and uploaded to the blockchain for evidence storage. Step S7: Send the final approved carbon emissions and other relevant data output in step S5 to the data application layer for real-time display, storage management, report generation and reporting. Step S8: Continuously run the credibility verification and self-diagnosis process: monitor the abnormal fluctuations of the status of the acquisition device and key calculation parameters, evaluate the credibility of the data and the rationality of the results based on preset rules, and output the verification results and necessary alarm information.

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