A carbon pollution emission on-line metering method and system linked with boiler working condition

Abstract

This application provides an online carbon pollution emission measurement method and system linked to boiler operating conditions. The method includes: generating a boiler operating condition state sequence based on fuel-side data, combustion-side data, and equipment operation status; determining the dynamic transmission delay between the boiler operating condition state sequence and emission-side data, and reconstructing the emission-side data according to the dynamic transmission delay to obtain operating condition-aligned emission observation data; determining the fuel carbon input based on the fuel-side data, and determining the flue gas carbon output based on the operating condition-aligned emission observation data; determining the particulate matter carbon emission by combining combustion-side data, flue gas-side data, purification-side data, and particulate matter carbon-related parameters; constructing a closed-loop carbon element measurement relationship, correcting the corresponding measurement parameters based on the closure residual of the closed-loop carbon element measurement relationship, and generating online carbon pollution emission measurement results associated with the operating condition segment based on the corrected measurement parameters. This application can improve the accuracy of carbon pollution measurement, enhance emission traceability, and improve the reliability of results.

Description

Technical Field

[0001] This application relates to the field of online boiler emission metering technology, and in particular to an online carbon pollution emission metering method and system that is linked to boiler operating conditions. Background Technology

[0002] Boiler combustion is a significant source of emissions in industrial production and energy conversion. After fuel burns in the furnace, it produces carbon-containing flue gas, incomplete combustion products, and particulate matter entrained in the smoke. With increasing demands for carbon emission accounting, coordinated pollutant control, and refined boiler operation, continuous, accurate, and traceable online metering of carbon emissions during boiler operation is necessary.

[0003] Existing technologies typically rely on continuous emission monitoring data at the chimney end to calculate emissions based on carbon dioxide, carbon monoxide, particulate matter concentrations, and flue gas flow rates, or to estimate carbon emissions based on fuel consumption and fixed emission factors. These methods primarily use emission-end monitoring results or fixed conversion parameters as the basis for measurement, without fully incorporating operating conditions such as boiler load changes, air-fuel ratio adjustments, burner operation, pulverizer activation / deactivation, soot blowing disturbances, and the status of flue gas purification equipment.

[0004] However, the transmission of changes in boiler combustion status to emission monitoring involves a time delay in flue gas transport. Directly using operating and emission data from the same moment for calculations can easily lead to emission attribution biases. Furthermore, total particulate matter concentration (TPC) is insufficient to characterize the carbonaceous components in flue gas, and fixed measurement parameters are ill-suited to the differences in emissions under steady-state combustion and transient disturbances. When online monitoring instruments experience drift, humidity interference, or sampling lag, existing methods lack a closed-loop verification mechanism based on fuel carbon input and multiple carbon outputs, resulting in insufficient accuracy and traceability of online carbon emission measurement results. Summary of the Invention

[0005] In view of this, the present application provides an online carbon pollution emission metering method and system that is linked to boiler operating conditions, in order to solve the problems of emission attribution deviation, inaccurate measurement of carbon components, and lack of closed-loop verification in the prior art.

[0006] A first aspect of this application provides an online carbon emission measurement method linked to boiler operating conditions, comprising: acquiring fuel-side data, combustion-side data, flue gas-side data, purification-side data, and emission-side data during boiler operation, and configuring time and quality identifiers; generating a boiler operating condition state sequence including operating condition segments and start and end times based on fuel-side data, combustion-side data, and equipment operation status; determining the dynamic transmission delay between the boiler operating condition state sequence and emission-side data based on flue gas transmission path parameters and real-time flue gas flow parameters, and reconstructing the emission-side data according to the dynamic transmission delay to obtain operating condition-aligned emission observation data; determining the fuel carbon input based on fuel-side data, and determining the flue gas carbon output based on operating condition-aligned emission observation data; determining the particulate matter carbon emission by combining combustion-side data, flue gas-side data, purification-side data, and particulate matter carbon-related parameters; constructing a closed-loop carbon element measurement relationship based on fuel carbon input, flue gas carbon output, and particulate matter carbon emission, correcting the corresponding measurement parameters based on the closed-loop residual of the closed-loop carbon element measurement relationship, and generating online carbon emission measurement results associated with operating condition segments based on the corrected measurement parameters.

[0007] A second aspect of this application provides an online carbon emission metering system linked to boiler operating conditions, comprising: an acquisition module for acquiring fuel-side data, combustion-side data, flue gas-side data, purification-side data, and emission-side data during boiler operation, and configuring time and quality identifiers; a generation module for generating a boiler operating condition state sequence containing operating condition segments and start and end times based on fuel-side data, combustion-side data, and equipment operation status; and a reconstruction module for determining the dynamic transmission delay between the boiler operating condition state sequence and emission-side data based on flue gas transmission path parameters and real-time flue gas flow parameters, and reconstructing the emission data according to the dynamic transmission delay. The system generates operating condition-aligned emission observation data from the combustion side data. A first determination module determines the fuel carbon input based on the fuel side data and the flue gas carbon output based on the operating condition-aligned emission observation data. A second determination module combines combustion side data, flue gas side data, purification side data, and particulate matter carbon-related parameters to determine the particulate matter carbon emissions. A correction module constructs a closed-loop carbon element measurement relationship based on the fuel carbon input, flue gas carbon output, and particulate matter carbon emissions, corrects the corresponding measurement parameters based on the closed-loop residual of the carbon element measurement relationship, and generates online carbon emission measurement results associated with the operating condition segment based on the corrected measurement parameters.

[0008] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: This application acquires fuel-side, combustion-side, flue gas-side, purification-side, and emission-side data during boiler operation and configures time and quality identifiers. Based on the fuel-side, combustion-side, and equipment operation status, a boiler operating condition sequence containing operating segment details and start / end times is generated. The dynamic transmission delay between the boiler operating condition sequence and emission-side data is determined based on flue gas transmission path parameters and real-time flue gas flow parameters. The emission-side data is then reconstructed according to the dynamic transmission delay to obtain operating condition-aligned emission observation data. The fuel carbon input is determined based on the fuel-side data, and the flue gas carbon output is determined based on the operating condition-aligned emission observation data. Combining the combustion-side, flue gas-side, and purification-side data with particulate matter carbon-related parameters, the particulate matter carbon emission is determined. A closed-loop carbon element measurement relationship is constructed based on the fuel carbon input, flue gas carbon output, and particulate matter carbon emission. The corresponding measurement parameters are corrected based on the closure residual of the closed-loop carbon element measurement relationship, and online carbon pollution emission measurement results associated with the operating segment are generated based on the corrected measurement parameters. This application can improve the accuracy of carbon pollution measurement, enhance emission traceability, and improve the reliability of results. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic flowchart of an online carbon pollution emission metering method linked to boiler operating conditions provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of an online carbon pollution emission metering system linked to boiler operating conditions, provided in an embodiment of this application. Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0011] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0012] In existing technologies, online metering of boiler carbon emissions typically relies primarily on continuous emission monitoring data from the chimney. This involves collecting data on carbon dioxide, carbon monoxide, particulate matter concentrations, and flue gas flow rates, and calculating emissions based on the concentration-flow conversion relationship. Some methods also incorporate fuel consumption and fixed emission factors to estimate carbon emissions. These methods largely depend on emission-end monitoring results or fixed conversion parameters, and generally do not incorporate boiler load changes, air-fuel ratio adjustments, combustion state fluctuations, equipment operating status, and flue gas purification processes into a unified online metering chain.

[0013] However, there is a time delay in the transmission of changes in boiler combustion status to the monitoring location at the chimney end. If boiler operating data and emission data at the same moment are used directly for calculation, it is easy to cause a discrepancy between the carbon pollution emission results and the actual combustion conditions. At the same time, particulate matter concentration usually only reflects the total amount of dust and is difficult to accurately characterize the carbon-containing components in the dust; fixed metering parameters are also difficult to adapt to emission changes under different operating conditions such as steady-state combustion, load changes, equipment operation, and flue gas purification disturbances. In addition, online monitoring instruments may be affected by factors such as drift, humidity interference, and sampling lag. Existing methods lack a closed-loop verification mechanism based on the relationship between fuel carbon input and multiple carbon outputs, resulting in insufficient accuracy, traceability, and reliability of online carbon pollution emission metering results.

[0014] To address the aforementioned issues, this application proposes an online carbon emission measurement method linked to boiler operating conditions. This method acquires fuel-side data, combustion-side data, flue gas-side data, purification-side data, and emission-side data during boiler operation, and configures time and quality identifiers. Based on the fuel-side data, combustion-side data, and equipment operation status, a boiler operating condition state sequence containing operating condition segments and their start and end times is generated. The dynamic transmission delay between the boiler operating condition state sequence and the emission-side data is determined based on flue gas transmission path parameters and real-time flue gas flow parameters. The emission-side data is then reconstructed according to the dynamic transmission delay to obtain operating condition-aligned emission observation data.

[0015] Based on this, this application determines the fuel carbon input based on fuel-side data and the flue gas carbon output based on operating condition aligned emission observation data; it also determines the particulate matter carbon emission by combining combustion-side data, flue gas-side data, purification-side data, and particulate matter carbon-related parameters; further, it constructs a closed-loop carbon element measurement relationship based on the fuel carbon input, flue gas carbon output, and particulate matter carbon emission, corrects the corresponding measurement parameters based on the closed-loop residual, and generates online carbon pollution emission measurement results associated with the operating condition segment based on the corrected measurement parameters.

[0016] Through the above technical solutions, this application can retrospectively match the emission monitoring results at the chimney end to the corresponding boiler operating condition segment, reducing the emission attribution deviation caused by flue gas transmission delay; it can combine particulate matter emission status and carbon-related parameters to determine the carbon emission of flue dust online, improving the accuracy of carbon component measurement; it can also use the closed measurement relationship of carbon elements to correct the measurement parameters, reducing the impact of monitoring data drift, humidity interference and sampling lag on the measurement results, thereby improving the accuracy of carbon pollution measurement, enhancing emission traceability and increasing the credibility of the results.

[0017] The technical solution of this application will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0018] Figure 1 This is a schematic flowchart of an online carbon pollution emission metering method linked to boiler operating conditions, provided in an embodiment of this application. Figure 1 As shown, the method may specifically include: S101, acquire fuel-side data, combustion-side data, flue gas-side data, purification-side data and emission-side data during boiler operation, and configure time and quality identifiers; S102, Generate a boiler operating condition sequence containing operating condition segments and start and end times based on fuel-side data, combustion-side data and equipment operation status; S103, Based on the flue gas transmission path parameters and real-time flue gas flow parameters, determine the dynamic transmission delay between the boiler operating condition state sequence and the emission side data, and reconstruct the emission side data according to the dynamic transmission delay to obtain operating condition aligned emission observation data; S104, determine the fuel carbon input based on fuel-side data, and determine the flue gas carbon output based on operating condition aligned emission observation data; S105, combining combustion-side data, flue gas-side data, purification-side data and particulate matter carbon-related parameters, determines the carbon emission of flue gas. S106: A closed-loop measurement relationship for carbon elements is constructed based on the carbon input of fuel, the carbon output of flue gas, and the carbon content of particulate matter. The corresponding measurement parameters are corrected according to the closed residual of the closed-loop measurement relationship for carbon elements, and online measurement results of carbon pollution emissions associated with the operating condition segment are generated based on the corrected measurement parameters.

[0019] In some embodiments, fuel-side data, combustion-side data, flue gas-side data, purification-side data, and emission-side data during boiler operation are acquired, and time and quality identifiers are configured, including: Establish a multi-source operation data acquisition channel based on the data correlation between the boiler combustion process, flue gas transport process, and emission monitoring process; The raw operational data of the corresponding measuring points is collected through the multi-source operational data acquisition channel, and the raw operational data is divided into fuel-side data, combustion-side data, flue gas-side data, purification-side data and emission-side data according to the source of the measuring points and data attributes. Based on the acquisition time, sampling period, and measurement point status, various types of data are time-series calibrated and validity determined, generating corresponding time and quality identifiers.

[0020] Specifically, when deploying an online metering system for boiler carbon emissions, a multi-source operational data acquisition channel is established, following the process of fuel entering the furnace, combustion and conversion within the furnace, and flue gas being transported to the emission monitoring terminal via the tail flue and purification equipment. This multi-source operational data acquisition channel includes data interfaces connecting to fuel metering devices, boiler distributed control systems, flue gas analyzers, dust removal and desulfurization control units, and continuous emission monitoring devices. Each interface is connected to the data acquisition server according to a unified measurement point code, enabling data from different sources to be identified, cached, and retrieved within the same metering cycle.

[0021] In a specific example, the target boiler is a coal-fired steam boiler. Fuel-side measurement points include cumulative coal feed rate, instantaneous coal feed rate, lower heating value of the coal entering the furnace, coal carbon content, coal moisture content, and coal bunker switching status. Combustion-side measurement points include boiler load, main steam flow rate, furnace outlet temperature, primary air volume, secondary air volume, total air volume, oxygen content, air-to-coal ratio, and pulverizer operating status. Flue gas-side measurement points include air preheater outlet flue gas temperature, tail flue gas velocity, flue gas pressure, flue gas humidity, and flue gas oxygen content. Purification-side measurement points include dust collector operating current, bag filter pressure differential, desulfurization tower inlet flue gas parameters, desulfurization tower outlet flue gas parameters, and bypass status. Emission-side measurement points include carbon dioxide concentration at the chimney end, carbon monoxide concentration, particulate matter concentration, flue gas flow rate, flue gas temperature, and flue gas humidity. The data acquisition server categorizes the raw operating data into fuel-side data, combustion-side data, flue gas-side data, purification-side data, and emission-side data according to the equipment source, physical meaning, and metering location of each measurement point.

[0022] To ensure that data from different sampling frequencies can participate in subsequent time delay reconstruction and operating condition alignment, the data acquisition server writes the acquisition time, reception time, sampling period, and measurement point identifier when receiving each piece of raw operating data, and generates a quality identifier based on the measurement point status. For second-level load, air volume, and oxygen data from distributed control systems, continuous time series are directly formed according to the acquisition time; for minute-level or batch-level coal quality data from coal quality detection systems, the data is extended to the corresponding fuel entry interval according to the coal bunker switching status and coal feeding records; for concentration and flow data returned by continuous emission monitoring devices, the validity of the data is determined by combining the sampling period, instrument calibration status, and data hold status.

[0023] Quality identifiers can be generated based on data integrity, range exceeding status, fluctuation amplitude, equipment maintenance status, and communication status. For example, when a flue gas flow meter is in normal communication and the value change is within the allowable range, the corresponding emission-side data is marked as valid; when a particulate matter monitor is in the backflushing maintenance or calibration maintenance phase, the corresponding particulate matter concentration data is marked as pending verification; when coal carbon content is missing but the most recent valid test record exists in the same coal bunker, the corresponding fuel-side data is marked as estimated and usable. When the measurement model subsequently calls up data, it can perform same-period matching based on the time identifier and determine whether the data participates in measurement, participates in measurement with reduced weight, or enters the verification and compensation process based on the quality identifier.

[0024] Through this embodiment, multi-source operational data completes source classification, time sequence calibration, and quality judgment before entering the online carbon emission metering process. This establishes a traceable data association between fuel input, combustion status, flue gas delivery, purification operation, and emission monitoring, which can reduce the impact of asynchronous sampling, abnormal measurement points, and data failure on subsequent operating condition identification, time delay reconstruction, and closed-loop carbon element metering.

[0025] In some embodiments, a boiler operating condition sequence including operating condition segments and start and end times is generated based on fuel-side data, combustion-side data, and equipment operating status, including: Extract operational change features from fuel-side and combustion-side data, and correlate these operational change features with equipment operating status over time to generate a working condition identification feature sequence; Based on the preset working condition discrimination rules, the working condition identification feature sequence is divided into states to determine the working condition continuity and working condition transition boundary between adjacent sampling periods. By merging continuous operating period periods based on the operating condition transition boundary, a boiler operating condition state sequence containing operating condition type, start and end time, and associated data index is generated.

[0026] Specifically, after completing the multi-source operational data acquisition and quality label configuration, the metering system reads fuel-side data, combustion-side data, and equipment operation status according to a unified metering cycle, and extracts change features from the continuously sampled data. Operational change features can include the rate of change in fuel input, changes in fuel calorific value correction, boiler load change rate, main steam flow rate change rate, furnace temperature fluctuation amplitude, oxygen content deviation, air-coal ratio change, total air volume change rate, and combustion stability characterization. Equipment operation status can include pulverizer activation / deactivation, coal feeder switching, burner adjustment, induced draft fan frequency adjustment, soot blowing, low-NOx combustion adjustment, and flue gas purification equipment status switching. The system maps various operational change features to the same timeline according to time labels and embeds equipment operation status as trigger points for operating condition changes into the corresponding sampling time periods, forming an operating condition identification feature sequence.

[0027] In a specific example, the target boiler maintained 70% load operation from 10:00 to 10:20. The fuel quantity, total air volume, oxygen content, and furnace temperature were all within the allowable fluctuation range, and the system marked this period as a candidate period for steady-state combustion. After 10:21, the load command increased, the coal feed rate and total air volume increased continuously, the main steam flow increased, and a short-term deviation occurred in the oxygen content. The system extracted the load increase characteristic and designated 10:21 as the candidate boundary for operating condition transition. At 10:27, the pulverizer start signal appeared, and the pulverized coal pipe air pressure and pulverizer speed changed synchronously. The system correlated the equipment's operating status with the load increase characteristic in time, forming a load increase identification segment with pulverizer start-up disturbance. At 10:38, the soot blowing valve action signal appeared, and abrupt changes occurred in the tail flue gas temperature and particulate matter correlation observations. The system separated this period from the previous combustion segment and identified it as a soot blowing disturbance segment.

[0028] The preset operating condition discrimination rules can comprehensively determine the condition based on the direction, magnitude, duration, and equipment operation status. When adjacent sampling periods have the same operating condition type and the difference between the operational change characteristics does not exceed the continuity threshold, the system determines that they belong to the same operating condition segment. When the change characteristics exceed the discrimination threshold, or when an equipment operation status with independent operating condition meaning appears, the system determines the corresponding sampling period as the operating condition transition boundary. For single-point abrupt changes caused by short-term noise, the system combines quality indicators and duration for filtering to avoid misjudging occasional data anomalies as operating condition switching.

[0029] After determining the operating condition transition boundary, the system merges consecutive operating condition periods in chronological order and configures the operating condition type, start time, end time, trigger characteristics, associated measurement point index, and data quality summary for each operating condition segment. The resulting boiler operating condition state sequence can serve as the basic data structure for subsequent dynamic transmission delay calculation, emission-side data backtracking and matching, and closed-loop carbon element measurement under different operating conditions.

[0030] Through this embodiment, the continuous operation process of the boiler is converted into a state sequence with clear start and end times and operating condition attributes. Fuel changes, combustion state changes and equipment operation states can be correlated and analyzed within the same time frame, thereby improving the accuracy of subsequent carbon pollution emission attribution, enhancing the ability to identify transient disturbances, and improving the process traceability of online metering results.

[0031] In some embodiments, the dynamic transmission delay between the boiler operating condition sequence and the emission-side data is determined based on flue gas transmission path parameters and real-time flue gas flow parameters, and the emission-side data is reconstructed according to the dynamic transmission delay to obtain operating condition aligned emission observation data, including: The flue gas transmission sections are constructed based on the flue gas transmission path parameters, and the residence time of each flue gas transmission section is determined based on the real-time flue gas flow parameters. By combining the operating condition change characteristics in the boiler operating condition state sequence, the dwell time of the section is dynamically corrected to generate the dynamic transmission delay of the corresponding operating condition segment. Based on the dynamic transmission delay, the emission-side data is backtracked and matched to the corresponding operating condition segment to generate operating condition-aligned emission observation data.

[0032] Specifically, after forming the boiler operating condition sequence, the metering system establishes a flue gas transmission path model based on the equipment layout relationship between the furnace outlet and the chimney emission monitoring point. The flue gas transmission path parameters may include the effective volume, flow cross-section, path length, local resistance, and equipment connection sequence of the furnace outlet flue, air preheater, dust collector, desulfurization tower, induced draft fan outlet flue, and chimney monitoring section. The system divides the transmission path into several flue gas transmission sections according to the actual flue gas flow direction, and configures inlet measuring points, outlet measuring points, and operating parameters that can be used for flow state correction for each section.

[0033] In a specific example, the flue gas from a coal-fired boiler passes sequentially from the furnace outlet through an air preheater, a bag filter, a desulfurization tower, and a chimney monitoring section. The system reads the flue gas temperature, pressure, velocity, flow rate, induced draft fan frequency, and differential pressure of the dust collector for each section, and calculates the basic residence time based on the effective volume of the section and the current flue gas volumetric flow rate. For the section from the air preheater outlet to the dust collector inlet, if a decrease in flue gas temperature leads to a change in flue gas volumetric flow rate, the system recalculates the residence time using the temperature and pressure corrected flue gas flow rate. For the desulfurization tower section, the system compensates for the basic residence time by considering the liquid-gas contact state, pressure drop inside the tower, and changes in outlet humidity. For the chimney monitoring section, the system determines the final sampling arrival time based on the length of the upstream straight pipe section of the monitoring platform and the flue gas velocity.

[0034] Under steady-state combustion conditions, the system accumulates the residence time of each section to obtain the basic dynamic transmission delay for the corresponding operating condition segment. Under transient conditions such as load increase, load decrease, coal mill commissioning, and soot blowing disturbance, the system further reads the operating condition change characteristics in the boiler operating condition state sequence and dynamically corrects the residence time of each section according to the load change rate, total air volume change rate, induced draft fan frequency change rate, and purification equipment resistance change. For example, at 10:21, the boiler enters the load increase condition, with continuous increases in coal feed and total air volume, and the induced draft fan frequency rising from 45Hz to 50Hz, reducing the basic transmission delay from 180 seconds to 150 seconds; at 10:38, soot blowing occurs, and particulate matter disturbance reaches the chimney monitoring section after passing through the tail flue and dust collector. The system backtracks the particulate matter observation data corresponding to this disturbance to the soot blowing disturbance segment according to the corrected delay.

[0035] During emission-side data reconstruction, the system uses dynamic transmission delay as an index to backtrack the carbon dioxide concentration, carbon monoxide concentration, particulate matter concentration, and flue gas flow rate at the chimney end from the emission monitoring time to the corresponding furnace operating time, and performs time window matching in conjunction with the sampling period. When an emission sampling window spans two operating condition segments, the system allocates observation data according to the window overlap ratio and data quality identifier, generating operating condition aligned emission observation data with operating condition segment number, backtracking time, observation weight, and quality status.

[0036] Through this embodiment, the emission monitoring results can be traced back to the corresponding boiler operating segment according to the actual flue gas transmission process, reducing the emission attribution deviation caused by direct matching at the same time. This ensures that the subsequent calculation of flue gas carbon output, determination of particulate carbon emissions, and carbon element closed correction have a consistent time series basis, thereby improving the accuracy of online carbon pollution emission measurement and process traceability.

[0037] In some embodiments, determining the fuel carbon input based on fuel-side data and determining the flue gas carbon output based on operating condition-aligned emission observation data includes: Based on the fuel metering data and fuel carbon content characterization data in the fuel-side data, the fuel carbon input is determined according to the operating condition segment; Standardized flue gas emission observation data are obtained by performing operating condition classification and state correction on the aligned emission observation data. Based on the carbon gas concentration and flue gas flow rate in the standardized flue gas emission observation data, determine the flue gas carbon output corresponding to the operating condition segment.

[0038] Specifically, after obtaining the aligned emission observation data for each operating condition, the metering system establishes metering windows for both the fuel-side and emission-side data according to the operating condition segments in the boiler operating condition sequence. The fuel carbon input is calculated based on the fuel quality and carbon content entering the furnace. The system first reads the instantaneous coal feed rate, cumulative coal feed rate, coal bunker switching records, and fuel batch information from the coal feeder. Then, it calls the carbon content characterization data from online coal quality testing results or the most recent valid test results to weight the supply ratio of different fuel sources within the same operating condition segment, thus obtaining the corresponding fuel carbon input for that operating condition segment.

[0039] In a specific example, a coal-fired boiler operates at increased load from 10:21 AM to 10:35 AM, with both coal bunkers A and B supplying coal simultaneously. Coal bunker A accounts for 60% of the coal supply, and coal bunker B accounts for 40%. The system determines the fuel quality entering the boiler during this operating segment based on the cumulative increment of the coal feeder and generates a fuel carbon content characterization value based on the carbon content, lower heating value, and moisture correction information corresponding to the two coal bunkers. When the online coal quality monitoring data of coal bunker A is valid, while the coal quality data of coal bunker B is in a delayed update state, the system uses the data from coal bunker A as the direct measurement data and corrects it by combining the most recent valid test data from coal bunker B with the current change in lower heating value, thereby obtaining the fuel carbon input during this increased load operating segment.

[0040] For operating condition aligned emission observation data, the system first confirms the attribution of emission observation data based on the operating condition segment number and backtracking time, and then performs state corrections on the carbon dioxide concentration, carbon monoxide concentration, and flue gas flow rate at the chimney end. State corrections include converting wet-based data to dry-based data, converting actual flue gas flow rate to standard-state flue gas flow rate, removing abnormal hold-up data, and downweighting low-quality data. If the humidity at the desulfurization tower outlet increases, causing changes in the moisture content of the flue gas, the system synchronously corrects the concentration and flow rate based on the flue gas temperature, pressure, and humidity. If the continuous emission monitoring device is in calibration hold-up mode, the corresponding sampling point does not directly participate in the calculation of flue gas carbon output; instead, it is interpolated or downweighted by adjacent valid observation windows.

[0041] Furthermore, after completing the state correction, the system calculates the carbon output corresponding to carbon dioxide and the carbon-converted output corresponding to carbon monoxide based on the dry basis concentrations of carbon dioxide and carbon monoxide and the standard state flue gas flow rate from the standardized flue gas emission observation data. These calculations are then integrated and summarized according to the start and end times of the operating condition segment to obtain the flue gas carbon output corresponding to that segment. For emission sampling windows spanning adjacent operating condition segments, the system allocates the carbon output according to the retrospective time overlap ratio and quality identifier to avoid emissions at load increases, load decreases, or equipment operation boundaries being classified into incorrect operating conditions.

[0042] In this embodiment, both fuel carbon input and flue gas carbon output are generated under the same operating condition segment. Fuel batch differences, coal quality update delays, flue gas humidity disturbances, and emission monitoring status can be uniformly corrected, so that subsequent calculation of particulate carbon emissions and closed-loop carbon element measurement have consistent data boundaries, improving the accuracy of online carbon emission measurement, consistency of operating condition attribution, and reliability of results.

[0043] In some embodiments, the carbon emissions of particulate matter are determined by combining combustion-side data, flue gas-side data, purification-side data, and particulate matter carbon-related parameters, including: Combustion state characterization related to particulate matter generation is extracted based on combustion-side data and flue gas-side data. By correlating combustion status characterization with purification-side data under operating conditions, the correction amount for particulate matter emissions is determined. Based on the particulate matter emission correction amount and particulate matter carbon content correlation parameters, the carbon content ratio of flue gas for the corresponding operating condition segment is generated, and the carbon emission amount of flue gas is determined based on the carbon content ratio of flue gas.

[0044] Specifically, after calculating the fuel carbon input and flue gas carbon output, the metering system further determines the particulate matter carbon emissions for each operating segment. The system first extracts boiler load, furnace outlet temperature, oxygen content deviation, air-fuel ratio, primary air volume, secondary air volume, pulverizer operating status, and burner adjustment status from combustion-side data. Then, it extracts particulate matter concentration, flue gas turbidity, flue gas velocity, flue gas humidity, tail flue temperature, and carbon monoxide concentration from flue gas-side data, forming a combustion state characterization related to particulate matter generation. This combustion state characterization reflects the degree of fuel burnout, the gas-solid carryover state in the furnace, and the tendency for incomplete combustion.

[0045] In a specific example, when the target boiler is operating at 70% load in steady state, the furnace outlet temperature remains stable, the oxygen content is within the normal range, the carbon monoxide concentration is low, and the particulate matter concentration changes gradually. The system characterizes the combustion state corresponding to this operating condition segment as a stable burnout state and calls the basic particulate matter carbon content correlation parameters under the stable operating condition. If the boiler experiences a rapid increase in coal feed, a lag in total air volume, an increase in carbon monoxide concentration, and a local decrease in furnace temperature during the load increase process, the system marks this operating condition segment as a state of incomplete burnout tendency and increases the weight of the unburned carbon correlation parameters in the estimation of the carbon content of flue gas. If the flue gas temperature in the tail flue flue flues flues, the particulate matter concentration increases briefly, and the flue gas turbidity spikes after the soot blowing action, the system correlates this change with the soot blowing disturbance condition to avoid directly equating the instantaneous increase in particulate matter with carbon-containing flue gas produced by combustion deterioration.

[0046] The system then correlates the combustion status characterization with the purification-side data. The purification-side data includes the dust collector's operating load, dust collector differential pressure, ash hopper discharge status, flue gas status at the desulfurization tower inlet and outlet, and bypass status. Based on the operating status of the purification equipment within the corresponding operating segment, the system corrects the particulate matter concentration at the emission end. For example, when the bag filter differential pressure increases and the cleaning action has just been completed, short-term fluctuations in particulate matter concentration may originate from equipment disturbances, and the system generates a purification disturbance correction; when the bypass status is abnormal or dust removal efficiency decreases, the system generates an emission penetration correction; when increased humidity causes dilution or condensation interference in particulate matter monitoring values, the system combines flue gas humidity and temperature to generate a humidity correction. These corrections collectively form the particulate matter emission correction.

[0047] Furthermore, after determining the particulate matter emission correction, the system calls upon particulate matter carbon content correlation parameters and, in conjunction with historical fly ash carbon content detection values, fuel ash characteristics, combustion completion characterization, and operating condition disturbance intensity, generates the dust carbon content ratio for the corresponding operating condition segment. For steady-state combustion conditions, the dust carbon content ratio is primarily determined by the basic carbon content correlation parameters; for conditions involving increased load, pulverizer commissioning, air-fuel ratio adjustment, and soot blowing disturbances, the system superimposes the operating condition disturbance correction onto the basic carbon content correlation parameters. Finally, the system combines the corrected particulate matter emission with the dust carbon content ratio to calculate the dust carbon emission within that operating condition segment, and stores the calculation result along with the operating condition segment number, particulate matter data quality identifier, and carbon content ratio source identifier.

[0048] In this embodiment, the carbon emission of flue gas is no longer directly replaced by the total concentration of particulate matter. Instead, it is determined by combining the combustion state, flue gas disturbance, purification equipment operation, and particulate matter carbon-related parameters under different operating conditions. This can improve the accuracy of flue gas carbon component measurement, reduce the impact of purification disturbance and humidity interference on the measurement results, and enhance the interpretability of the online carbon emission measurement results under different operating conditions.

[0049] In some embodiments, a closed-loop carbon metering relationship is constructed based on fuel carbon input, flue gas carbon output, and particulate matter carbon emissions, including: The amount of carbon input in the fuel is used as the carbon input boundary for the corresponding operating condition segment, and the amount of carbon output in the flue gas and the amount of carbon emissions in the particulate matter are used as the carbon output boundary for the corresponding operating condition segment. The carbon output boundary is compensated by combining the amount of unburned carbon and transient carbon accumulation during the boiler combustion process. Based on the carbon budget relationship between the compensated carbon output boundary and the carbon input boundary, a closed-loop metric relationship for carbon elements is generated.

[0050] Specifically, after obtaining the fuel carbon input, flue gas carbon output, and particulate carbon emissions, the metering system constructs a closed-loop carbon metering relationship segment by segment according to the boiler operating condition sequence. The system first determines the fuel carbon input entering the furnace within the corresponding operating condition segment as the carbon input boundary, and then determines the flue gas carbon output and particulate carbon emissions obtained after dynamic time delay reconstruction as the carbon output boundaries, ensuring that both carbon input and carbon output fall within the same operating condition time window. For data crossing operating condition boundaries, the system allocates data according to the backtracking time, sampling window overlap ratio, and quality identifier to avoid mixing carbon amounts between adjacent operating conditions.

[0051] In a specific example, the target boiler is in a load-increasing condition from 10:21 to 10:35. The system obtains the fuel carbon input for this operating segment based on the cumulative increase of the coal feeder and the coal carbon content characterization data. It also obtains the carbon dioxide and carbon monoxide equivalents based on the emission observation data aligned with the operating condition, and the particulate matter carbon emission amount based on the particulate matter emission correction and the carbon content ratio of flue gas. Since the air-coal ratio, furnace temperature, and flue gas flow rate are all changing during the load-increasing phase, some fuel carbon may exist in the form of unburned carbon in fly ash, unburned carbon in slag, carbon deposited in the flue, or carbon retained in the flue gas. The system further uses the unburned carbon correlation quantity and transient carbon accumulation quantity to compensate for the carbon output boundary.

[0052] The amount of unburned carbon can be determined based on historical fly ash carbon content, slag emission records, combustion completion characterization, and carbon monoxide concentration changes. When fly ash carbon detection is delayed, the system generates an estimated value of unburned carbon based on current oxygen deviation, furnace temperature fluctuations, and changes in the air-coal ratio, and uses a quality identifier to distinguish between directly detected and estimated values. Transient carbon accumulation can be determined based on flue gas transmission delay, effective flue volume, ash hopper storage status, dust collector cleaning cycle, and ash discharge status. For example, during soot blowing disturbance segments, particulate matter emissions may temporarily increase, but ash discharge from the ash hopper may not be completed synchronously. In this case, the system will use a portion of the carbon as transient carbon accumulation for compensation, rather than directly attributing it all to immediate emissions at the chimney end.

[0053] After compensation, the system performs carbon budget matching between the compensated carbon output boundary and the carbon input boundary, generating a closed-loop carbon element measurement relationship that includes carbon input, flue gas carbon output, particulate matter carbon output, unburned carbon compensation, transient accumulation compensation, and closed-loop residual. If the deviation between the carbon input boundary and the compensated carbon output boundary for a certain operating condition segment is within the allowable range, the operating condition segment enters the effective measurement state; if the deviation exceeds the allowable range, the system transmits the closed-loop residual to the subsequent measurement parameter correction process.

[0054] Through this embodiment, fuel carbon input, flue gas carbon output, particulate carbon output, unburned carbon compensation, and transient accumulation compensation can form a closed constraint within the same operating condition segment, so that the carbon pollution emission measurement results have a clear carbon budget basis, which can improve the accuracy of online measurement, reduce the deviation caused by transient operation and unburned carbon lag, and enhance the verifiability and reliability of the measurement results.

[0055] In some embodiments, the corresponding measurement parameters are corrected based on the closed residual of the carbon element closed measurement relationship, and online carbon emission measurement results associated with the operating condition segment are generated based on the corrected measurement parameters, including: Calculate the closure residual between the carbon input boundary and the compensated carbon output boundary based on the closed stoichiometric relationship of carbon elements; Based on the closed residual, quality label and the working condition attributes of the corresponding working condition segment, the source of measurement deviation is determined and the measurement parameters corresponding to the source of measurement deviation are corrected. The corrected metering parameters are used to update the flue gas carbon output and particulate carbon emissions, generating online carbon emission metering results that include operating condition attribution and metering reliability identifiers.

[0056] Specifically, after generating the closed-loop carbon measurement relationship, the metering system calculates the closure residual between the carbon input boundary and the compensated carbon output boundary segment by segment according to the operating condition. The closure residual can be expressed as the difference between the fuel carbon input and the flue gas carbon output, particulate carbon emissions, unburned carbon associated amount, and transient carbon accumulation. The system compares the closure residual with the allowable residual range corresponding to the operating condition segment and, in conjunction with quality indicators, determines whether the source of the residual is related to data loss, instrument drift, humidity disturbance, coal quality estimation deviation, or sudden changes in operating conditions.

[0057] In a specific example, the target boiler was operating at increased load from 10:21 to 10:35, resulting in a significant increase in fuel carbon input. The carbon dioxide and carbon monoxide values, after dynamic time-delay reconstruction, increased synchronously, but the closure residual remained above the allowable range for the increased load condition. Further quality checks revealed valid flue gas flow meter data, a normal carbon dioxide analyzer, coal carbon content derived from the most recent valid test data, and the particulate matter monitor not under maintenance. Simultaneously, the estimated particulate matter carbon ratio did not increase synchronously with deviations in carbon monoxide and oxygen concentrations. Based on this, the system determined the source of the measurement deviation to be a low particulate matter carbon ratio under transient incomplete combustion conditions and corrected the particulate matter carbon ratio parameters and unburned carbon compensation parameters corresponding to the increased load condition.

[0058] If the closed residual shows a shift trend synchronized with humidity changes, the system will point the source of the deviation to the humidity correction parameter and re-correct the dry basis concentration and standard state flue gas flow rate using flue gas temperature, pressure, and humidity. If the closed residual continues to deviate in the same direction in multiple steady-state operating segments, and the fuel carbon input mass identifier is available for estimation, the system will point the source of the deviation to the fuel carbon content characterization data and update the fuel carbon input correction parameter based on coal bunker switching records, changes in lower heating value, and historical test data. If the closed residual is synchronized with changes in induced draft fan frequency and dust collector differential pressure, the system will correct the dynamic transmission delay parameter or particulate matter emission correction parameter.

[0059] After identifying the source of the deviation, the system calls the corrected measurement parameters to recalculate the flue gas carbon output and particulate carbon emissions within the corresponding operating segment, and updates the closed-loop measurement relationship for carbon elements. For operating segments where the residuals converge to the allowable range, the system generates valid measurement results; for operating segments where the residuals are still large, the system generates a measurement reliability indicator based on data quality, residual size, and parameter correction magnitude. The final online carbon emission measurement results include the operating segment number, operating attribute, fuel carbon input, flue gas carbon output, particulate carbon emissions, closed-loop residuals, parameter correction records, and measurement reliability indicator.

[0060] In this embodiment, the closed residual is no longer just used as an anomaly indicator, but is used to identify the source of measurement deviation and drive the correction of the corresponding measurement parameters. This enables the flue gas carbon output and particulate carbon emissions to be updated online according to changes in operating conditions, data quality, and residuals, thereby improving the accuracy of carbon pollution emission measurement, the reliability of operating condition attribution, and the credibility of results.

[0061] In some embodiments, after correcting the measurement parameters corresponding to the source of the measurement deviation, the method further includes: Establish a sub-condition metering parameter library according to the condition attributes of the condition segments, and bind the metering parameters to the corresponding condition attributes; Based on the closure residual, quality label, and metrological reliability label, stable calibration samples and transient disturbance samples are selected from the operating condition segments; The basic measurement parameters in the sub-operating condition measurement parameter library are updated based on stable calibration samples, and the disturbance correction parameters under the corresponding operating condition attributes are updated based on transient disturbance samples, so that subsequent operating condition segments can call the updated sub-operating condition measurement parameter library to generate online carbon pollution emission measurement results.

[0062] Specifically, after completing the metering parameter correction, the metering system establishes a sub-condition metering parameter library according to the operating condition attributes in the boiler operating condition state sequence. The sub-condition metering parameter library can set parameter units according to operating condition categories such as steady-state combustion, load increase, load decrease, coal mill operation / deactivation, air-coal ratio adjustment, soot blowing disturbance, and purification equipment disturbance. Each parameter unit records dynamic transmission delay parameters, flue gas state correction parameters, particulate matter emission correction parameters, flue dust carbon content parameters, unburned carbon compensation parameters, transient carbon accumulation parameters, and closed-loop residual tolerance parameters. When generating online metering results for a specific operating condition segment, the metering system first calls the corresponding parameter unit based on the operating condition attributes. After parameter correction, the correction source, correction magnitude, applicable operating condition, and update time are written into the parameter unit, ensuring that the metering parameters are bound to the specific operating condition attributes.

[0063] In a specific example, during steady-state operation of the target boiler at 70% load, the closed residual between the fuel carbon input and the compensated carbon output boundary remained continuously within the allowable range. Fuel-side, flue gas-side, and emission-side data all possessed valid quality indicators, and the measurement reliability was marked as high. The system selected this operating condition segment as a stable calibration sample and used a smooth update method to correct the basic dynamic transmission delay, flue gas flow correction coefficient, and basic particulate carbon content in the steady-state combustion parameter unit. Because the operating condition fluctuations corresponding to the stable calibration sample were small, the system only allowed the basic measurement parameters to be updated in small steps to avoid parameter jumps caused by a single sample.

[0064] When the boiler load increases from 70% to 90%, the coal feed rate, total air volume, induced draft fan frequency, and carbon monoxide concentration all change continuously. The closed-loop residual increases rapidly, but the quality indicator does not show any instrument abnormalities. The system identifies this segment as a transient disturbance sample and associates it with the load increase condition parameter unit. For this sample, the system does not directly overwrite the basic metering parameters, but instead extracts the load change rate, air-coal ratio deviation, oxygen deviation, particulate matter concentration change, and residual convergence time to update the disturbance correction parameters under the load increase condition. Similarly, when soot blowing causes short-term fluctuations in the flue gas temperature and particulate matter concentration in the tail flue, the system writes this segment into the soot blowing disturbance parameter unit to update the particulate matter emission correction parameters and transient carbon accumulation parameters.

[0065] In subsequent measurement cycles, if a new operating condition segment is identified as having the same operating condition attributes, the system will prioritize calling the corresponding parameter unit in the sub-operating condition measurement parameter library and make fine adjustments based on the current quality identifier and real-time closure residual. When the sample reliability is low, the closure residual is abnormally amplified, or the data quality identifier shows a maintenance, hold, or out-of-limit status, the system will only save the abnormal record of that segment and will not use it for parameter updates.

[0066] Through this embodiment, the measurement parameters can be stored hierarchically and updated differently according to the operating condition attributes. Stable operating condition samples are used to calibrate the basic measurement parameters, and transient disturbance samples are used to correct the disturbance response parameters. This enables the online carbon pollution emission measurement model to adapt to changes in coal quality, load adjustment, equipment operation and purification disturbances, thereby improving the accuracy, stability and operating condition adaptability of subsequent measurement results.

[0067] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of this application.

[0068] Figure 2 This is a schematic diagram of an online carbon pollution emission metering system linked to boiler operating conditions, provided in an embodiment of this application. Figure 2 As shown, the system includes: The acquisition module 201 is used to acquire fuel-side data, combustion-side data, flue gas-side data, purification-side data and emission-side data during boiler operation, and configure time and quality identifiers. The generation module 202 is used to generate a boiler operating condition state sequence containing operating condition segments and start and end times based on fuel-side data, combustion-side data and equipment operation status. The reconstruction module 203 is used to determine the dynamic transmission delay between the boiler operating condition state sequence and the emission side data based on the flue gas transmission path parameters and real-time flue gas flow parameters, and to reconstruct the emission side data according to the dynamic transmission delay to obtain operating condition aligned emission observation data. The first determining module 204 is used to determine the amount of fuel carbon input based on fuel-side data and to determine the amount of flue gas carbon output based on operating condition aligned emission observation data. The second determining module 205 is used to determine the carbon emission of flue gas by combining combustion-side data, flue gas-side data, purification-side data and particulate matter carbon-related parameters. The correction module 206 is used to construct a closed-loop measurement relationship for carbon elements based on the carbon input of fuel, carbon output of flue gas and carbon emissions of particulate matter, correct the corresponding measurement parameters according to the closed residual of the closed-loop measurement relationship for carbon elements, and generate online measurement results of carbon pollution emissions associated with the operating condition segment based on the corrected measurement parameters.

[0069] In some embodiments, Figure 2 The acquisition module 201 establishes a multi-source operation data acquisition channel based on the data correlation between the boiler combustion process, flue gas transport process, and emission monitoring process. It collects the original operation data of the corresponding measuring points through the multi-source operation data acquisition channel, and divides the original operation data into fuel-side data, combustion-side data, flue gas-side data, purification-side data, and emission-side data according to the source of the measuring points and data attributes. Based on the acquisition time, sampling period, and measuring point status, it performs time-series calibration and validity determination on various types of data, and generates corresponding time identifiers and quality identifiers.

[0070] In some embodiments, Figure 2 The generation module 202 extracts the operational change features from the fuel-side data and combustion-side data, and correlates the operational change features with the equipment action status over time to generate a working condition identification feature sequence; it divides the working condition identification feature sequence into states based on preset working condition discrimination rules to determine the working condition continuity and working condition transition boundary between adjacent sampling periods; it merges continuous working condition periods according to the working condition transition boundary to generate a boiler working condition state sequence containing working condition type, start and end time, and associated data index.

[0071] In some embodiments, Figure 2The reconstruction module 203 constructs flue gas transmission sections based on flue gas transmission path parameters and determines the corresponding residence time of each flue gas transmission section based on real-time flue gas flow parameters; combined with the operating condition change characteristics in the boiler operating condition state sequence, the residence time of the sections is dynamically corrected to generate the dynamic transmission delay of the corresponding operating condition segment; the emission side data is backtracked and matched to the corresponding operating condition segment according to the dynamic transmission delay to generate operating condition aligned emission observation data.

[0072] In some embodiments, Figure 2 The first determining module 204 determines the fuel carbon input based on the fuel metering data and fuel carbon content characterization data in the fuel-side data, according to the operating condition segment; performs operating condition assignment and state correction on the operating condition aligned emission observation data to obtain standardized flue gas emission observation data; and determines the flue gas carbon output corresponding to the operating condition segment based on the carbon-containing gas concentration and flue gas flow rate in the standardized flue gas emission observation data.

[0073] In some embodiments, Figure 2 The second determining module 205 extracts combustion state characteristics related to particulate matter generation based on combustion-side data and flue gas-side data; it correlates the combustion state characteristics with the purification-side data under operating conditions to determine the particulate matter emission correction amount; based on the particulate matter emission correction amount and particulate matter carbon content correlation parameters, it generates the smoke carbon content ratio for the corresponding operating condition segment, and determines the smoke carbon emission amount based on the smoke carbon content ratio.

[0074] In some embodiments, Figure 2 The correction module 206 uses the fuel carbon input as the carbon input boundary of the corresponding operating condition segment and the flue gas carbon output and particulate carbon emissions as the carbon output boundary of the corresponding operating condition segment; it compensates the carbon output boundary by combining the unburned carbon correlation amount and transient carbon accumulation amount during boiler combustion; and it generates a closed-loop measurement relationship of carbon elements based on the carbon budget relationship between the compensated carbon output boundary and the carbon input boundary.

[0075] In some embodiments, Figure 2 The correction module 206 calculates the closure residual between the carbon input boundary and the compensated carbon output boundary based on the closed measurement relationship of carbon elements; based on the closure residual, quality identifier and operating condition attributes of the corresponding operating condition segment, it determines the source of measurement deviation and corrects the measurement parameters corresponding to the source of measurement deviation; it calls the corrected measurement parameters to update the flue gas carbon output and particulate carbon emissions, and generates online carbon pollution emission measurement results including operating condition attribution and measurement reliability identifier.

[0076] In some embodiments, Figure 2The correction module 206 establishes a sub-operating condition measurement parameter library according to the operating condition attributes of the operating condition segment, and binds the measurement parameters to the corresponding operating condition attributes; based on the closure residual, quality identifier and measurement reliability identifier, it selects stable calibration samples and transient disturbance samples from the operating condition segment; based on the stable calibration samples, it updates the basic measurement parameters in the sub-operating condition measurement parameter library, and based on the transient disturbance samples, it updates the disturbance correction parameters under the corresponding operating condition attributes, so that subsequent operating condition segments can call the updated sub-operating condition measurement parameter library to generate online carbon emission measurement results.

[0077] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0078] Figure 3 This is a schematic diagram of the electronic device 3 provided in an embodiment of this application. Figure 3 As shown, the electronic device 3 of this embodiment includes: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program 303, it implements the steps in the various method embodiments described above. Alternatively, when the processor 301 executes the computer program 303, it implements the functions of each module / unit in the various system embodiments described above.

[0079] Electronic device 3 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 3 may include, but is not limited to, processor 301 and memory 302. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or different components.

[0080] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0081] The memory 302 can be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. The memory 302 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 3. The memory 302 can also include both internal and external storage units of the electronic device 3. The memory 302 is used to store computer programs and other programs and data required by the electronic device.

[0082] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0083] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a readable storage medium (e.g., a computer-readable storage medium). Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which may be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0084] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An online carbon pollution emission measurement method linked to boiler operating conditions, characterized in that, include: Acquire fuel-side data, combustion-side data, flue gas-side data, purification-side data, and emission-side data during boiler operation, and configure time and quality identifiers; A boiler operating condition sequence containing operating condition segments and start and end times is generated based on the fuel-side data, the combustion-side data, and the equipment operation status. The dynamic transmission delay between the boiler operating condition sequence and the emission side data is determined based on the flue gas transmission path parameters and real-time flue gas flow parameters, and the emission side data is reconstructed according to the dynamic transmission delay to obtain operating condition aligned emission observation data. The fuel carbon input is determined based on the fuel-side data, and the flue gas carbon output is determined based on the operating condition aligned emission observation data. By combining the combustion-side data, the flue gas-side data, the purification-side data, and the particulate matter carbon content correlation parameters, the carbon emission content of flue gas is determined. A closed-loop measurement relationship for carbon elements is constructed based on the fuel carbon input, the flue gas carbon output, and the particulate matter carbon emissions. The corresponding measurement parameters are corrected according to the closed-loop residual of the carbon element closed-loop measurement relationship, and online measurement results of carbon pollution emissions associated with the operating condition segment are generated based on the corrected measurement parameters.

2. The method according to claim 1, characterized in that, The acquisition of fuel-side data, combustion-side data, flue gas-side data, purification-side data, and emission-side data during boiler operation, and the configuration of time and quality identifiers, includes: Establish a multi-source operation data acquisition channel based on the data correlation between the boiler combustion process, flue gas transport process, and emission monitoring process; The original operating data of the corresponding measuring points is collected through the multi-source operating data acquisition channel, and the original operating data is divided into fuel-side data, combustion-side data, flue gas-side data, purification-side data and emission-side data according to the source of the measuring points and data attributes. Based on the acquisition time, sampling period, and measurement point status, various types of data are time-series calibrated and validity determined, generating corresponding time and quality identifiers.

3. The method according to claim 1, characterized in that, The step of generating a boiler operating condition sequence containing operating condition segments and start and end times based on the fuel-side data, the combustion-side data, and the equipment operating status includes: Extract operational change features from the fuel-side data and the combustion-side data, and correlate the operational change features with the equipment's operating status over time to generate a working condition identification feature sequence; The working condition identification feature sequence is divided into states based on the preset working condition discrimination rules to determine the working condition continuity and working condition transition boundary between adjacent sampling periods. Based on the operating condition transition boundary, continuous operating condition time periods are merged to generate the boiler operating condition state sequence, which includes operating condition type, start and end time, and associated data index.

4. The method according to claim 1, characterized in that, The dynamic transmission delay between the boiler operating condition sequence and the emission-side data is determined based on flue gas transmission path parameters and real-time flue gas flow parameters. The emission-side data is then reconstructed according to the dynamic transmission delay to obtain operating condition-aligned emission observation data, including: The flue gas transmission path parameters are used to construct flue gas transmission sections, and the corresponding section residence time for each flue gas transmission section is determined based on the real-time flue gas flow parameters. Based on the operating condition change characteristics in the boiler operating condition state sequence, the dwell time of the section is dynamically corrected to generate the dynamic transmission delay of the corresponding operating condition segment. The emission-side data is backtracked and matched to the corresponding operating condition segment according to the dynamic transmission delay, thereby generating the operating condition aligned emission observation data.

5. The method according to claim 1, characterized in that, The step of determining the fuel carbon input based on the fuel-side data and determining the flue gas carbon output based on the operating condition aligned emission observation data includes: Based on the fuel metering data and fuel carbon content characterization data in the fuel-side data, the fuel carbon input is determined according to the operating condition segment. The operating condition aligned emission observation data are subjected to operating condition attribution and state correction to obtain standardized flue gas emission observation data. Based on the carbon gas concentration and flue gas flow rate in the standardized flue gas emission observation data, determine the flue gas carbon output corresponding to the operating condition segment.

6. The method according to claim 1, characterized in that, The determination of carbon emissions from flue gas by combining the combustion-side data, the flue gas-side data, the purification-side data, and particulate matter carbon content correlation parameters includes: Based on the combustion-side data and the flue gas-side data, a combustion state characterization related to particulate matter generation is extracted; The combustion state characterization is correlated with the purification side data under operating conditions to determine the particulate matter emission correction amount. Based on the particulate matter emission correction amount and the particulate matter carbon content correlation parameters, the carbon content ratio of flue gas for the corresponding operating condition segment is generated, and the carbon emission amount of flue gas is determined based on the carbon content ratio of flue gas.

7. The method according to claim 1, characterized in that, The construction of a closed-loop carbon element measurement relationship based on the fuel carbon input, the flue gas carbon output, and the particulate matter carbon emissions includes: The fuel carbon input is used as the carbon input boundary of the corresponding operating condition segment, and the flue gas carbon output and the particulate carbon emission are used as the carbon output boundary of the corresponding operating condition segment. The carbon output boundary is compensated by combining the amount of unburned carbon and the amount of transient carbon accumulation during the boiler combustion process. Based on the carbon budget relationship between the compensated carbon output boundary and the carbon input boundary, the closed-loop metric relationship for carbon elements is generated.

8. The method according to claim 7, characterized in that, The step of correcting the corresponding measurement parameters based on the closed residual of the carbon element closed measurement relationship, and generating online carbon emission measurement results associated with the operating condition segment based on the corrected measurement parameters, includes: Calculate the closure residual between the carbon input boundary and the compensated carbon output boundary based on the closed stoichiometric relationship of the carbon element; Based on the closed residual, the quality identifier, and the operating condition attributes of the corresponding operating condition segment, the source of measurement deviation is determined, and the measurement parameters corresponding to the source of measurement deviation are corrected. The corrected metering parameters are used to update the carbon output of the flue gas and the carbon emissions of the particulate matter, generating online metering results of the carbon pollution emissions that include operating condition attribution and metering reliability identifier.

9. The method according to claim 8, characterized in that, After correcting the measurement parameters corresponding to the source of the measurement deviation, the method further includes: A sub-condition metering parameter library is established according to the condition attributes of the aforementioned condition segments, and the metering parameters are bound to the corresponding condition attributes; Based on the closed residual, the quality identifier, and the measurement reliability identifier, stable calibration samples and transient disturbance samples are selected from the operating condition segments; Based on the stable calibration sample, the basic measurement parameters in the sub-operating condition measurement parameter library are updated, and based on the transient disturbance sample, the disturbance correction parameters under the corresponding operating condition attribute are updated, so that subsequent operating condition segments can call the updated sub-operating condition measurement parameter library to generate the online measurement results of carbon pollution emissions.

10. An online carbon emission metering system linked to boiler operating conditions, characterized in that, include: The acquisition module is used to acquire fuel-side data, combustion-side data, flue gas-side data, purification-side data, and emission-side data during boiler operation, and configure time and quality identifiers. The generation module is used to generate a boiler operating condition state sequence containing operating condition segments and start and end times based on the fuel-side data, the combustion-side data, and the equipment operation status. The reconstruction module is used to determine the dynamic transmission delay between the boiler operating condition state sequence and the emission side data based on the flue gas transmission path parameters and real-time flue gas flow parameters, and to reconstruct the emission side data according to the dynamic transmission delay to obtain operating condition aligned emission observation data. The first determining module is used to determine the fuel carbon input based on the fuel-side data and to determine the flue gas carbon output based on the operating condition aligned emission observation data. The second determining module is used to determine the carbon emission of flue gas by combining the combustion-side data, the flue gas-side data, the purification-side data, and the particulate matter carbon content correlation parameters. The correction module is used to construct a closed-loop carbon element measurement relationship based on the fuel carbon input, the flue gas carbon output, and the particulate matter carbon emissions, correct the corresponding measurement parameters according to the closed-loop residual of the carbon element measurement relationship, and generate online carbon pollution emission measurement results associated with the operating condition segment based on the corrected measurement parameters.

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