A carbon emission data monitoring and analysis system and method

Abstract

The present invention relates to a carbon emission data monitoring and analysis system and method. This system addresses the problem of mixed flue gas from a multi-unit thermal power plant being unable to be traced to the source of a single unit. The system includes: a data acquisition module that obtains flue gas data from the total chimney outlet through a CEMS, calculates precise carbon emissions using humidity and dust concentration correction models, and simultaneously collects power generation, coal consumption, and concentration curves; an anomaly judgment module that comprehensively determines whether an anomaly exists based on the corresponding carbon emissions per unit power generation and coal consumption, and the distortion rate of the concentration curve; an anomaly location module that injects tracer gas into each unit and detects the tracer gas concentration at the total outlet, uses a deconvolution algorithm to infer the emission contribution rate of each unit, and identifies abnormal units; and a cause investigation module that determines an investigation sequence based on historical data and matches suspected causes. The present invention achieves decoupling and precise traceability of carbon emission data from a single unit in mixed flue gas, enabling rapid anomaly location and improving the low-carbon intelligence level of power plants.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emissions, and in particular to a carbon emissions data monitoring and analysis system and method. Background Art

[0002] With growing global concern about climate change, carbon emissions monitoring has become a critical component in achieving the "dual carbon" goals. Thermal power generation, a key area of ​​carbon emissions within the energy industry, faces a particularly pressing need for accurate monitoring. Currently, greenhouse gas emissions from coal-fired units require real-time, accurate measurement and traceability. Traditional, extensive emission management models are no longer sufficient for this refined control, necessitating the establishment of an efficient and reliable carbon emissions monitoring system.

[0003] In the existing technology, the continuous emission monitoring system (CEMS) based on direct measurement has become the mainstream solution for real-time monitoring of carbon emissions in thermal power plants. This technology installs high-precision sensors at emission sources such as boiler flues and chimneys to measure the carbon emissions in the flue gas. The online measurement of key parameters such as concentration and flue gas flow rate, and the calculation of carbon emissions based on real-time data, has significant advantages in real-time, accuracy and continuity.

[0004] For example, the existing Chinese patent with publication number CN113282868A discloses an online monitoring system and calculation and analysis method for the carbon emission intensity per kilowatt-hour of a thermal power plant, which includes: a flue gas measuring device monitors the concentration and flow of greenhouse gases emitted by the thermal power plant in real time, and transmits the data to the comprehensive carbon emission monitoring module; a thermal power plant power generation meter monitors the real-time power generation of the thermal power plant generator set in real time, and transmits the data to the comprehensive carbon emission monitoring module; the comprehensive carbon emission monitoring module monitors the real-time power generation of the thermal power plant generator set in real time according to the monitored data. The real-time total carbon emissions of the boiler flue gas are calculated by the concentration and the total flow rate of the boiler flue gas, and then divided by the real-time power generation of the thermal power plant generator set to obtain the real-time carbon emission intensity per kilowatt-hour of the thermal power plant. This invention realizes accurate real-time online monitoring of the carbon emission intensity of thermal power plants.

[0005] However, in thermal power plants with multiple units operating in parallel, existing CEMS technology faces significant application bottlenecks: the flue gases from multiple units are collected and discharged through the same chimney, and the data collected by CEMS is the comprehensive value of the mixed flue gas, which cannot achieve independent separation and accurate traceability of carbon emission data for individual units. Specifically,

[0006] (1) Ambiguous location of pollution sources: When the system detects abnormal carbon emissions, it is impossible to quickly determine which unit has excessive emissions or abnormal operation problems;

[0007] (2) Low inspection efficiency: All units need to be inspected one by one, increasing operation and maintenance costs and time costs;

[0008] (3) The treatment plan lacks specificity: it is impossible to formulate a precise emission reduction strategy based on the emission characteristics of a single unit, which affects the overall emission reduction efficiency.

[0009] In response to the technical pain points of "data mixing and difficulty in tracing the source of a single unit" in the carbon emission monitoring of the above-mentioned multi-unit thermal power plant, the present invention aims to provide a carbon emission data monitoring and analysis system and method. Through flue gas fingerprint marking and dynamic tracing, the decoupling and precise tracing of the carbon emission data of each unit in the mixed flue gas can be achieved, providing technical support for the refined management of carbon emissions, rapid investigation of pollution sources and optimization of emission reduction plans in thermal power plants, and promoting the industry's transformation to low-carbon and intelligent operation. Summary of the Invention

[0010] In response to the above problems, the present invention proposes a carbon emission data monitoring and analysis system and method to realize the function of carbon emission monitoring and analysis.

[0011] The technical solution adopted by the present invention to solve the technical problem is as follows: the present invention provides a carbon emission data monitoring and analysis system, comprising:

[0012] Data acquisition module: collects the flue gas data from the total outlet of the chimney of the thermal power plant with multiple units during the detection period to calculate the carbon emissions, and corrects the calculation results according to the flue gas humidity and dust concentration, and simultaneously obtains the power generation, coal consumption and Concentration curve.

[0013] Abnormal judgment module: Based on the carbon emissions corresponding to unit power generation and unit coal consumption and The concentration curve distortion rate is used to comprehensively judge whether carbon emissions are abnormal. If so, the abnormality positioning module is executed.

[0014] Abnormal location module: A set concentration of tracer gas is injected into the emission pipe of each unit and the total concentration of the tracer gas is detected at the main chimney outlet. The emission contribution rate of each unit is calculated based on the deconvolution algorithm. The carbon emission proportion of each unit is analyzed according to the emission contribution rate of each unit and compared with its carbon emission proportion reference range. Units with abnormal carbon emissions are identified and marked.

[0015] Cause investigation module: Determine the order of troubleshooting the causes of abnormal carbon emissions based on historical abnormal carbon emissions fault diagnosis data, match the suspected causes of abnormal carbon emissions units according to the signature characteristics of the causes of abnormal carbon emissions and provide feedback.

[0016] The present invention provides a carbon emission data monitoring and analysis method, comprising the following steps:

[0017] Step 1: Collect the flue gas data from the total outlet of the chimney of the thermal power plant with multiple units during the detection period to calculate the carbon emissions, and modify the calculation results according to the flue gas humidity and dust concentration, and simultaneously obtain the power generation, coal consumption and Concentration curve.

[0018] Step 2: Based on the carbon emissions corresponding to unit power generation and unit coal consumption and Concentration curve distortion rate, comprehensively judge whether carbon emissions are abnormal. If so, execute Step 3.

[0019] Step 3: Inject a set concentration of tracer gas into the emission pipe of each unit and detect the total concentration of tracer gas at the total chimney outlet. Calculate the emission contribution rate of each unit based on the deconvolution algorithm. Analyze the carbon emission proportion of each unit based on its emission contribution rate and compare it with its carbon emission proportion reference range. Identify and mark units with abnormal carbon emissions.

[0020] Step 4: Determine the order of troubleshooting the causes of abnormal carbon emissions based on historical abnormal carbon emissions fault diagnosis data. According to the signature characteristics of the causes of abnormal carbon emissions, match the suspected causes of the abnormal carbon emissions units and provide feedback.

[0021] Compared with the prior art, the carbon emission data monitoring and analysis system and method described in the present invention has the following beneficial effects:

[0022] 1. Accurately trace carbon emissions from individual units: This invention injects tracer gas into the emission pipes of each unit and detects the tracer gas concentration at the total chimney outlet. Combined with a deconvolution algorithm, this method decouples the carbon emission data of each unit from the mixed flue gas. This solves the difficulty in tracing the source of a single unit caused by the mixing of flue gases from multiple units, accurately identifies units with abnormal carbon emissions, and avoids ambiguous positioning of pollution sources.

[0023] 2. Improve troubleshooting efficiency: When carbon emissions are abnormal, the present invention can quickly locate the abnormal unit by comparing the carbon emission ratio of each unit with the reference range, eliminating the need to check all units one by one, thereby improving troubleshooting efficiency and shortening fault diagnosis time.

[0024] 3. Improve data accuracy: This invention corrects the carbon emission calculation results based on flue gas humidity and dust concentration, reducing the calculation deviation caused by environmental factors, and pre-processes the carbon dioxide concentration curve to improve data accuracy, making the judgment of carbon emission anomalies more reliable.

[0025] 4. Multi-dimensional comprehensive judgment: The present invention comprehensively evaluates the carbon emission anomaly coefficient from three dimensions: total emission, fuel combustion, and flue gas composition, overcoming the limitations of a single indicator and improving the accuracy of anomaly identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 This is a system module connection diagram of the present invention.

[0028] Figure 2 Schematic diagram of the method of the present invention.

[0029] Figure 3 This is a flow chart for determining the carbon emission ratio of a unit in the abnormality locating module of the present invention.

[0030] Figure 4 This is a flow chart of the algorithm for solving the emission contribution rate of each unit using the constrained least squares method of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0032] See also Figure 1 As shown, the present invention provides a carbon emission data monitoring and analysis system, which includes a data acquisition module, an abnormality judgment module, an abnormality positioning module, and a cause investigation module.

[0033] The abnormality judgment module is connected to the data acquisition module and the abnormality positioning module respectively, and the cause investigation module is connected to the abnormality positioning module.

[0034] The data acquisition module collects the flue gas data of the total outlet of the chimney of the multi-unit thermal power plant during the detection period to calculate the carbon emissions, and corrects the calculation results according to the flue gas humidity and dust concentration, and simultaneously obtains the power generation, coal consumption and Concentration curve.

[0035] Furthermore, the specific process of obtaining the carbon emissions of the thermal power plant during the detection period in the data acquisition module is as follows: through the flue gas continuous emission monitoring system installed at the chimney main outlet, the total chimney outlet data of the same batch of coal-fired power generation in the multi-unit thermal power plant during the detection period are obtained. The theoretical carbon emissions of the thermal power plant during the detection period are obtained based on the average values ​​of the concentration and flue gas flow, combined with the duration of the detection period, and the set calculation formula of the theoretical carbon emissions.

[0036] The theoretical carbon emissions calculated based on the formula and the actual carbon emissions of the total chimney outlet of each historical power generation of the thermal power plant are obtained and compared to obtain the carbon emission deviation of each historical power generation of the thermal power plant.

[0037] And obtain the humidity and dust concentration of the total exhaust gas of the chimney of each historical power generation of the thermal power plant.

[0038] A training set is constructed based on the carbon emission deviations of each historical power generation of the thermal power plant and the corresponding flue gas humidity and dust concentration.

[0039] According to the training set, based on the single variable principle, with flue gas humidity as input and carbon emission deviation as output, the correlation model between flue gas humidity and carbon emission deviation is obtained through machine learning algorithm.

[0040] Similarly, the correlation model between dust concentration and carbon emission deviation is obtained.

[0041] The humidity and dust concentration of the flue gas at the total chimney outlet during the detection period are obtained, and the correlation model between the two and the carbon emission deviation is substituted respectively to obtain the corresponding carbon emission deviations and accumulate them to obtain the carbon emission deviation during the detection period.

[0042] The theoretical carbon emissions of the thermal power plant during the detection period are corrected according to the carbon emissions deviation during the detection period to obtain the carbon emissions of the thermal power plant during the detection period.

[0043] It should be noted that, in a specific embodiment, the calculation formula for theoretical carbon emissions is: , where represents the theoretical carbon emissions, express concentration, Indicates the flue gas flow rate, Indicates the running time, Indicates the set conversion factor.

[0044] It should be noted that the sensors arranged at the chimney main exhaust outlet detect the humidity and dust concentration of the flue gas at the chimney main exhaust outlet.

[0045] It's important to note that flue gas humidity and dust concentration are crucial factors in calculating carbon emissions from thermal power plants, requiring corrections based on their impact on emission characteristics. Flue gas humidity can alter the total flue gas volume, leading to biased carbon emissions. High humidity dilutes the carbon dioxide concentration in the flue gas, leading to an underestimation of actual carbon emissions if not corrected. Dust can also carry incompletely burned carbon components, and the carbon content in dust can lead to incomplete carbon emissions statistics.

[0046] Furthermore, the data acquisition module obtains the The specific process of the concentration curve is: setting each sampling time point within the detection period according to the preset equal time interval principle.

[0047] The total chimney outlet at each sampling time point within the detection period is obtained through the continuous flue gas emission monitoring system. The concentration was preprocessed, including missing value processing and outlier processing.

[0048] The sampling time point is the horizontal axis, The concentration is used as the vertical coordinate to establish a coordinate system, and the total chimney outlet at each sampling time point in the detection period after pretreatment is used. Concentration, plotting the detection period Concentration curve.

[0049] It should be noted that the specific process of missing value processing is: if the data of a certain sampling time point is missing, the data of the previous sampling time point and the next sampling time point adjacent to the sampling time point are averaged, and the result of the average calculation is used as the data of the sampling time point.

[0050] It should be noted that the specific process of the outlier processing is: according to the data of each sampling time point, a linear regression line corresponding to the data group is drawn based on a mathematical model establishment method, the deviation of the data of each sampling time point relative to the linear regression line is obtained and compared with the deviation threshold. If the deviation of the data at a certain sampling time point exceeds the threshold, the data at the sampling time point is abnormal, and the data at the sampling time point is replaced by the mean of the data of the previous sampling time point and the next sampling time point adjacent to the sampling time point.

[0051] It should be noted that the collected Concentration data is preprocessed to improve The accuracy of concentration curve drawing makes the The abnormal carbon emission judgment results of the concentration curve are more reliable.

[0052] In this example, the present invention corrects the carbon emission calculation results based on the flue gas humidity and dust concentration, reduces the calculation deviation caused by environmental factors, and pre-processes the carbon dioxide concentration curve to improve data accuracy, making the judgment of carbon emission anomalies more reliable.

[0053] The abnormality judgment module is based on the carbon emissions corresponding to the unit power generation and unit coal consumption and The concentration curve distortion rate is used to comprehensively judge whether carbon emissions are abnormal. If so, the abnormality positioning module is executed.

[0054] Furthermore, the specific process of analyzing the carbon emissions corresponding to the unit power generation in the abnormality judgment module is: obtaining the carbon emissions corresponding to the unit power generation in the detection period according to the carbon emissions and power generation in the detection period.

[0055] Obtain the carbon emissions corresponding to unit power generation in the same historical period and the industry benchmark value of carbon emissions corresponding to unit power generation.

[0056] The carbon emissions corresponding to unit power generation during the detection period are compared with the historical data of the same period and the industry benchmark value to obtain the first relative deviation and second relative deviation of the carbon emissions corresponding to unit power generation.

[0057] According to the quantitative mapping relationship between the two relative deviations of the carbon emissions corresponding to the set unit power generation and the carbon emission anomaly factor based on the total emission index, the carbon emission anomaly factor based on the total emission index within the detection period is obtained.

[0058] Furthermore, the specific process of analyzing the carbon emissions corresponding to unit coal consumption in the abnormality judgment module is: obtaining the carbon emissions corresponding to unit coal consumption in the detection period according to the carbon emissions and coal consumption in the detection period.

[0059] Obtain the coal quality information of the current batch of coal and compare it with the coal quality information of each historical power generation, and analyze the similarity between the coal quality of each historical power generation and the coal quality of the current batch of coal.

[0060] The historical power generation corresponding to the maximum coal quality similarity is recorded as the reference historical power generation.

[0061] The carbon emissions corresponding to unit coal consumption in the historical power generation process are obtained and recorded as the reference value of carbon emissions corresponding to unit coal consumption.

[0062] The carbon emissions per unit of coal consumption during the detection period are compared with its reference value to obtain the relative deviation of the carbon emissions per unit of coal consumption.

[0063] According to the quantitative mapping relationship between the relative deviation of the carbon emissions corresponding to the set unit coal consumption and the carbon emission anomaly factor based on the fuel combustion index, the carbon emission anomaly factor based on the fuel combustion index within the detection period is obtained.

[0064] It should be noted that the similarity between the quality of coal for power generation in history and the quality of coal for the current batch of coal is analyzed by comparing each sub-item in the coal quality information for power generation in history with each sub-item in the coal quality information for the current batch of coal one by one, obtaining the deviation of each sub-item in the coal quality information for power generation in history relative to the current batch of coal, and setting the weight of each sub-item in the coal quality information based on the credibility of the coal quality similarity assessment of each sub-item in the coal quality information, and the cumulative sum is 1.

[0065] Based on the deviation of each sub-item in the historical coal quality information for power generation relative to the current batch of coal and the weight of each sub-item, the similarity between the coal quality of each historical power generation and the coal quality of the current batch is evaluated through weighted fusion analysis.

[0066] Furthermore, the abnormality judgment module analyzes The specific process of concentration curve distortion rate is: obtain the concentration curve distortion rate within the detection period The maximum fluctuation of the concentration curve and obtain The concentration curve corresponds to the slope of the linear regression line.

[0067] Obtain the historical power generation process with normal carbon emissions in the same period The concentration curve was further obtained. The maximum fluctuation of the concentration curve and the slope of the linear regression line are recorded as The baseline value for the fluctuation of the concentration curve and the slope of the linear regression line.

[0068] Within the detection period The maximum fluctuation of the concentration curve and the slope of the linear regression line are compared with the corresponding reference values ​​to obtain the overshoot of the fluctuation and the slope of the linear regression line.

[0069] According to the setting The functional relationship between the concentration curve fluctuation and the overshoot of the linear regression line slope and the distortion rate is obtained. Concentration curve distortion rate.

[0070] according to Concentration curve distortion rate, combined with the set The carbon emission anomaly factor based on the flue gas composition index corresponding to the concentration curve distortion rate range is matched to obtain the carbon emission anomaly factor based on the flue gas composition index within the detection period.

[0071] It should be noted that obtaining The specific method of the maximum fluctuation of the concentration curve is: The difference between the value of each data point on the concentration curve and the value of its adjacent data point is recorded as the fluctuation of each data point. The fluctuations of each data point are compared with each other to obtain the maximum fluctuation of the data point and recorded as The maximum fluctuation of the concentration curve.

[0072] It should be noted that the overshoot of the fluctuation amount and the slope of the linear regression line refers to the amount by which the fluctuation amount and the slope of the linear regression line exceed their reference values.

[0073] Furthermore, the specific process of judging whether carbon emissions are abnormal in the abnormality judgment module is: according to the carbon emission abnormality factor based on the total emission index, fuel combustion index, and flue gas composition index during the detection period, combined with the set weights of the total emission index, fuel combustion index, and flue gas composition index, the carbon emission abnormality coefficient during the detection period is evaluated through weighted fusion analysis.

[0074] The carbon emission anomaly coefficient during the detection period is compared with the preset carbon emission anomaly coefficient threshold. If the carbon emission anomaly coefficient during the detection period is greater than the threshold, the carbon emission is abnormal.

[0075] It should be noted that the weights of the total emission index, fuel combustion index, and flue gas composition index are set according to their importance in assessing whether carbon emissions are abnormal, and their cumulative sum is 1.

[0076] In this example, the present invention comprehensively judges whether carbon emissions are abnormal from three dimensions: total emission index, fuel combustion index, and flue gas composition index. It can overcome the limitations of a single indicator and improve the accuracy of anomaly identification through multi-dimensional cross-validation.

[0077] The abnormality locating module injects a set concentration of tracer gas into the emission pipe of each unit and detects the total concentration of the tracer gas at the total chimney outlet. Based on the deconvolution algorithm, the emission contribution rate of each unit is calculated. According to the emission contribution rate of each unit, its carbon emission proportion is analyzed and compared with its carbon emission proportion reference range, and the units with abnormal carbon emissions are identified and marked.

[0078] Further, see Figure 3 As shown, the specific working process of the abnormality positioning module is: a tracer gas of a set concentration is injected into the independent exhaust pipe of each unit through a tracer gas injection device, and a high-precision mass spectrometer is deployed at the main exhaust outlet of the chimney to detect the total concentration of the tracer gas.

[0079] Building a target model , where Indicates the total concentration of tracer gas detected at the total chimney outlet. Indicates the The concentration of the tracer gas injected into each unit, , Indicates the first Emission contribution rate of each unit.

[0080] See Figure 4 As shown in Figure 2, the emission contribution rate of each unit is solved using the constrained least squares method.

[0081] The carbon emissions of each unit are obtained by multiplying the emission contribution rate of each unit by the total carbon emissions from the chimney during the detection period.

[0082] Calculate the ratio of the carbon emissions of each unit to the carbon emissions during the detection period to obtain the carbon emission proportion of each unit.

[0083] The carbon emission ratio of each unit is compared with the reference range set for its carbon emission ratio. If the carbon emission ratio of a unit exceeds the reference range of its carbon emission ratio, the carbon emission of the unit is abnormal.

[0084] Count and mark the units with abnormal carbon emissions.

[0085] It should be noted that the tracer gas injection device is connected to the exhaust pipe of each generator set and can control the injection of tracer gas into the exhaust pipe of each generator set separately.

[0086] It should be noted that the tracer gas is a chemically inert, easily detectable, non-toxic gas or a perfluorocarbon. In a specific embodiment, the tracer gas is .

[0087] It should be noted that the trace tracer gas and the flue gas are mixed at a constant ratio according to a set ratio.

[0088] It should be noted that the process of analyzing the unit's emission contribution rate based on tracer gas marking and then determining the unit's carbon emission share can be carried out multiple times to eliminate accidental errors.

[0089] It should be noted that the constrained least squares method is used to solve the emission contribution rate of each unit. The specific process is as follows:

[0090] S1: Build target model , where Indicates the total concentration of tracer gas detected at the total chimney outlet. Indicates the The concentration of the tracer gas injected into each unit, , Indicates the first Emission contribution rate of each unit.

[0091] S2: Determine the constraints, including:

[0092] Normalization constraints for emission contribution rate: .

[0093] Boundary constraints: .

[0094] S3: Minimize the residual sum of squares between the measured value and the model prediction , where for feature matrix, for Emission contribution rate vector.

[0095] S4: Introducing Lagrange multipliers Dealing with normalization constraints:

[0096] .

[0097] right Find partial derivatives ,in .

[0098] right Find partial derivatives .

[0099] Write the system of equations in augmented matrix form .

[0100] Solving the linear equations yields , ,judge Is it satisfied If the conditions are met, output , and obtain the emission contribution rate of each unit.

[0101] If the conditions are not met, use the projected gradient method to iteratively adjust and re-solve the linear equations until the conditions are met and output , and obtain the emission contribution rate of each unit.

[0102] It should be noted that a simulated operation process, in a specific embodiment, a multi-unit thermal power plant has three units, and the tracer gas concentration detected at the chimney outlet is , the tracer gas concentrations injected into the independent gas paths of the three units are The specific process of solving the emission contribution rate of each unit using the constrained least squares method is as follows:

[0103] Constructing the augmented matrix .

[0104] based on Construct the system of equations:

[0105] .

[0106] The solution is , All satisfied .

[0107] based on verify , that is, the emission contribution rates of the three units are 0.35, 0.30, and 0.35 respectively.

[0108] It should be noted that according to the calculation formula of carbon emission ratio It can be concluded that the emission contribution rate of each unit is the carbon emission proportion of each unit. In the above specific embodiment, the carbon emission proportions of the three units are 35%, 30%, and 35% respectively.

[0109] In this example, the present invention injects tracer gas into the exhaust pipe of each unit and detects the tracer gas concentration at the total exhaust outlet of the chimney. It combines the deconvolution algorithm to calculate the emission contribution rate, thereby decoupling the carbon emission data of each unit in the mixed flue gas, solving the problem of difficulty in tracing the source of a single unit caused by the mixing of flue gases from multiple units, accurately identifying units with abnormal carbon emissions, and avoiding ambiguous positioning of pollution sources.

[0110] In this example, when carbon emissions are abnormal, the present invention can quickly locate the abnormal unit by comparing the carbon emission ratio of each unit with the reference range, without having to check all units one by one, thereby improving the troubleshooting efficiency and shortening the fault diagnosis time.

[0111] The cause investigation module determines the order of investigating the causes of abnormal carbon emissions based on historical abnormal carbon emissions fault diagnosis data, matches the suspected causes of the abnormal carbon emissions units according to the signature characteristics of the causes of abnormal carbon emissions and provides feedback.

[0112] Furthermore, the specific process of the cause investigation module is: according to the historical carbon emission abnormality fault diagnosis data, the causes corresponding to the historical carbon emission abnormalities of the thermal power plant units are obtained, and the historical fault counts corresponding to each cause of the carbon emission abnormality are classified and counted.

[0113] Arrange the causes of carbon emission abnormalities in descending order according to their corresponding historical fault counts to obtain the troubleshooting order of the causes of carbon emission abnormalities.

[0114] Obtain the signature characteristics of each cause of abnormal carbon emissions.

[0115] The causes of abnormal carbon emission units shall be investigated in order. If the abnormal carbon emission units have the characteristic signs of a certain cause, the cause shall be classified as the suspected cause of the abnormal carbon emission units.

[0116] Suspected causes of abnormal carbon emissions from each unit are counted, where the suspected causes may be one or more combinations.

[0117] It should be noted that the characteristic features of various causes of abnormal carbon emissions are summarized based on historical experience.

[0118] In a specific embodiment, the abnormal carbon emission of the unit is caused by the decline in boiler combustion efficiency, which is characterized by increased exhaust gas temperature, deviation of flue gas oxygen content from the optimal value, and fly ash combustible content exceeding the design value.

[0119] In a specific embodiment, the abnormal carbon emission of the unit is caused by abnormal desulfurization and denitrification systems, and its characteristic features include a year-on-year increase in power consumption of the desulfurization system, an increase in ammonia escape rate of the denitrification system, and a year-on-year increase in ammonia water consumption.

[0120] In a specific embodiment, the abnormal carbon emission of the unit is caused by the decrease in thermal efficiency of the turbine, and its characteristic features are increased heat consumption rate of the turbine, increased main steam flow but the unit load is not increased synchronously, and decreased cylinder efficiency.

[0121] See also Figure 2 As shown, the present invention provides a carbon emission data monitoring and analysis method, comprising the following steps:

[0122] Step 1: Collect the flue gas data from the total outlet of the chimney of the thermal power plant with multiple units during the detection period to calculate the carbon emissions, and modify the calculation results according to the flue gas humidity and dust concentration, and simultaneously obtain the power generation, coal consumption and Concentration curve.

[0123] Step 2: Based on the carbon emissions corresponding to unit power generation and unit coal consumption and Concentration curve distortion rate, comprehensively judge whether carbon emissions are abnormal. If so, execute Step 3.

[0124] Step 3: Inject a set concentration of tracer gas into the emission pipe of each unit and detect the total concentration of tracer gas at the total chimney outlet. Calculate the emission contribution rate of each unit based on the deconvolution algorithm. Analyze the carbon emission proportion of each unit based on its emission contribution rate and compare it with its carbon emission proportion reference range. Identify and mark units with abnormal carbon emissions.

[0125] Step 4: Determine the order of troubleshooting the causes of abnormal carbon emissions based on historical abnormal carbon emissions fault diagnosis data. According to the signature characteristics of the causes of abnormal carbon emissions, match the suspected causes of the abnormal carbon emissions units and provide feedback.

[0126] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0127] The above embodiments may be implemented in whole or in part through software, hardware, firmware or any other combination. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0128] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0129] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0130] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0131] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A carbon emission data monitoring and analysis system, characterized in that: include: Data acquisition module: collects the flue gas data from the total outlet of the chimney of the thermal power plant with multiple units during the detection period to calculate the carbon emissions, and corrects the calculation results according to the flue gas humidity and dust concentration, and simultaneously obtains the power generation, coal consumption and concentration curve; Abnormal judgment module: Based on the carbon emissions corresponding to unit power generation and unit coal consumption and Concentration curve distortion rate, comprehensively judge whether carbon emissions are abnormal. If so, execute the abnormality positioning module; otherwise, operate normally; Abnormal Location Module: This module injects a set concentration of tracer gas into the exhaust pipe of each unit and detects the total tracer gas concentration at the chimney outlet. The module then uses a deconvolution algorithm to calculate the emission contribution rate of each unit. The module then analyzes the carbon emission percentage of each unit based on its emission contribution rate and compares it with its reference range. This module then identifies and marks units with abnormal carbon emissions. Cause investigation module: Determine the order of troubleshooting the causes of abnormal carbon emissions based on historical abnormal carbon emissions fault diagnosis data, match the suspected causes of abnormal carbon emissions units according to the signature characteristics of the causes of abnormal carbon emissions and provide feedback.

2. A carbon emission data monitoring and analysis system according to claim 1, characterized in that: The specific process of obtaining the carbon emissions of the thermal power plant during the detection period in the data acquisition module is as follows: Through the continuous flue gas emission monitoring system installed at the chimney main outlet, the total chimney outlet gas flow rate during the detection period of the same batch of coal-fired power generation in a multi-unit thermal power plant is obtained. The theoretical carbon emissions of the thermal power plant during the detection period are obtained based on the average values ​​of the concentration and flue gas flow, the duration of the detection period, and the set theoretical carbon emissions calculation formula; Obtain the theoretical carbon emissions calculated based on the formula and the actual carbon emissions of the total chimney outlet of each historical power generation of the thermal power plant and compare them to obtain the carbon emission deviation of each historical power generation of the thermal power plant; And obtain the humidity and dust concentration of the total exhaust gas from the chimney of each historical power generation in the thermal power plant; A training set is constructed based on the carbon emission deviations of each historical power generation of the thermal power plant and the corresponding flue gas humidity and dust concentration; Based on the training set and the single variable principle, with flue gas humidity as input and carbon emission deviation as output, a correlation model between flue gas humidity and carbon emission deviation is obtained through a machine learning algorithm. Similarly, the correlation model between dust concentration and carbon emission deviation is obtained; Obtain the humidity and dust concentration of the smoke at the total chimney outlet during the detection period, substitute them into the correlation model between the two and the carbon emission deviation, obtain the corresponding carbon emission deviations, and add them up to obtain the carbon emission deviation during the detection period; The theoretical carbon emissions of the thermal power plant during the detection period are corrected according to the carbon emissions deviation during the detection period to obtain the carbon emissions of the thermal power plant during the detection period.

3. The carbon emission data monitoring and analysis system according to claim 1, characterized in that: The data acquisition module obtains the detection period The specific process of the concentration curve is: Set each sampling time point within the detection cycle according to the preset equal time interval principle; The total chimney outlet at each sampling time point within the detection period is obtained through the continuous flue gas emission monitoring system. concentration and perform preprocessing, wherein the preprocessing includes missing value processing and outlier processing; The sampling time point is the horizontal axis, The concentration is used as the vertical coordinate to establish a coordinate system, and the total chimney outlet at each sampling time point in the detection period after pretreatment is used. Concentration, plotting the detection period Concentration curve.

4. The carbon emission data monitoring and analysis system according to claim 1, characterized in that: The specific process of analyzing the carbon emissions corresponding to unit power generation in the abnormality judgment module is as follows: The carbon emissions corresponding to the unit power generation during the detection period are obtained according to the carbon emissions and power generation during the detection period; Obtain the carbon emissions per unit of electricity generated during the same historical period and the industry benchmark value for carbon emissions per unit of electricity generated; Compare the carbon emissions corresponding to the unit power generation during the detection period with the historical data of the same period and the industry benchmark value, and obtain the first relative deviation and second relative deviation of the carbon emissions corresponding to the unit power generation; According to the quantitative mapping relationship between the two relative deviations of the carbon emissions corresponding to the set unit power generation and the carbon emission anomaly factor based on the total emission index, the carbon emission anomaly factor based on the total emission index within the detection period is obtained.

5. A carbon emission data monitoring and analysis system according to claim 4, characterized in that: The specific process of analyzing the carbon emissions corresponding to unit coal consumption in the abnormality judgment module is as follows: The carbon emissions corresponding to the unit coal consumption during the detection period are obtained according to the carbon emissions and coal consumption during the detection period; Obtain the coal quality information of the current batch of coal and compare it with the coal quality information of each historical power generation, and analyze the similarity between the coal quality of each historical power generation and the coal quality of the current batch of coal; The historical power generation corresponding to the maximum coal quality similarity is recorded as the reference historical power generation; Obtain the carbon emissions per unit of coal used in the historical power generation process, and record it as the reference value of carbon emissions per unit of coal used; Compare the carbon emissions per unit of coal consumption during the test period with its reference value to obtain the relative deviation of the carbon emissions per unit of coal consumption; According to the quantitative mapping relationship between the relative deviation of the carbon emissions corresponding to the set unit coal consumption and the carbon emission anomaly factor based on the fuel combustion index, the carbon emission anomaly factor based on the fuel combustion index within the detection period is obtained.

6. A carbon emission data monitoring and analysis system according to claim 5, characterized in that: The abnormality judgment module analyzes The specific process of concentration curve distortion rate is: Get the detection period The maximum fluctuation of the concentration curve and obtain The slope of the linear regression line corresponding to the concentration curve; Get The specific method of the maximum fluctuation of the concentration curve is: The difference between the value of each data point on the concentration curve and the value of its adjacent data point is recorded as the fluctuation of each data point. The fluctuations of each data point are compared with each other to obtain the maximum fluctuation of the data point and recorded as The maximum fluctuation of the concentration curve; Obtain the historical power generation process with normal carbon emissions in the same period The concentration curve was further obtained. The maximum fluctuation of the concentration curve and the slope of the linear regression line are recorded as Baseline values ​​for concentration curve fluctuation and linear regression line slope; Within the detection period The maximum fluctuation of the concentration curve and the slope of the linear regression line are compared with the corresponding reference values ​​to obtain the overshoot of the fluctuation and the slope of the linear regression line; According to the setting The functional relationship between the concentration curve fluctuation and the overshoot of the linear regression line slope and the distortion rate is obtained. Concentration curve distortion rate; according to Concentration curve distortion rate, combined with the set The carbon emission anomaly factor based on the flue gas composition index corresponding to the concentration curve distortion rate range is matched to obtain the carbon emission anomaly factor based on the flue gas composition index within the detection period.

7. A carbon emission data monitoring and analysis system according to claim 6, characterized in that: The specific process of judging whether carbon emissions are abnormal in the abnormality judgment module is as follows: Based on the carbon emission anomaly factor based on the total emission index, fuel combustion index, and flue gas composition index during the detection period, combined with the set weights of the total emission index, fuel combustion index, and flue gas composition index, the carbon emission anomaly coefficient during the detection period is evaluated through weighted fusion analysis; The carbon emission anomaly coefficient during the detection period is compared with the preset carbon emission anomaly coefficient threshold. If the carbon emission anomaly coefficient during the detection period is greater than the threshold, the carbon emission is abnormal.

8. The carbon emission data monitoring and analysis system according to claim 1, characterized in that: The specific working process of the abnormality positioning module is as follows: A tracer gas injection device is used to inject a set concentration of tracer gas into the independent exhaust pipe of each unit, and a high-precision mass spectrometer is deployed at the main exhaust outlet of the chimney to detect the total concentration of the tracer gas; Building a target model , where Indicates the total concentration of tracer gas detected at the total chimney outlet. Indicates the The concentration of the tracer gas injected into each unit, , Indicates the first Emission contribution rate of each unit; The constrained least squares method is used to solve the emission contribution rate of each unit; The carbon emissions of each unit are calculated by multiplying the emission contribution rate of each unit by the total carbon emissions from the chimney during the detection period. Calculate the ratio of each unit's carbon emissions to the carbon emissions during the testing period to obtain the carbon emission proportion of each unit; Compare the carbon emission ratio of each unit with the reference range set for its carbon emission ratio. If the carbon emission ratio of a unit exceeds the reference range, the carbon emission of the unit is considered abnormal. Count and mark the units with abnormal carbon emissions.

9. The carbon emission data monitoring and analysis system according to claim 1, characterized in that: The specific process of the cause troubleshooting module is as follows: Based on the historical carbon emission abnormality fault diagnosis data, the causes corresponding to each historical carbon emission abnormality of the thermal power plant units are obtained, and the historical fault counts corresponding to each cause of carbon emission abnormality are classified and counted; Arrange the causes of carbon emission abnormalities in descending order according to the number of corresponding historical failures, and obtain the troubleshooting order of the causes of carbon emission abnormalities; Obtain the signature characteristics of each cause of abnormal carbon emissions; Investigate the causes of abnormal carbon emission units in order. If the abnormal carbon emission units have the characteristic of a certain cause, classify the cause as a suspected cause of the abnormal carbon emission units. Suspected causes of abnormal carbon emissions from each unit are counted, where the suspected causes may be one or more combinations.

10. A carbon emission data monitoring and analysis method, characterized in that: The steps include: Step 1: Collect the flue gas data from the total outlet of the chimney of the thermal power plant with multiple units during the detection period to calculate the carbon emissions, and modify the calculation results according to the flue gas humidity and dust concentration, and simultaneously obtain the power generation, coal consumption and concentration curve; Step 2: Based on the carbon emissions corresponding to unit power generation and unit coal consumption and Concentration curve distortion rate, comprehensively judge whether carbon emissions are abnormal. If so, execute Step 3; Step 3: Inject a set concentration of tracer gas into the exhaust pipe of each unit and measure the total tracer gas concentration at the chimney outlet. Calculate the emission contribution rate of each unit using a deconvolution algorithm. Analyze the carbon emission proportion of each unit based on its emission contribution rate and compare it with its reference range. Identify and mark units with abnormal carbon emissions. Step 4: Determine the order of troubleshooting the causes of abnormal carbon emissions based on historical abnormal carbon emissions fault diagnosis data. According to the signature characteristics of the causes of abnormal carbon emissions, match the suspected causes of the abnormal carbon emissions units and provide feedback.

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