Carbon emission on-line monitoring method based on unit load dynamic adjustment velocity field coefficient

Abstract

The invention provides a unit load dynamic adjustment velocity field coefficient-based carbon emission online monitoring method, and relates to the technical field of carbon emission measurement, and the method comprises the steps: receiving the real-time unit load of a coal-fired power plant; the real-time unit load is input to a dynamic speed field coefficient model, a target speed field coefficient corresponding to the real-time unit load is output, the dynamic speed field coefficient model is pre-constructed based on a unit load sample and a speed field coefficient sample, and the dynamic speed field coefficient model is pre-constructed based on the unit load sample and the speed field coefficient sample; the velocity field coefficient sample is obtained by comparing flow velocity data measured by a three-dimensional pitot tube under multiple groups of different load conditions with flow velocity data measured by original flow velocity measurement equipment; and based on the target velocity field coefficient, the velocity measured by the velocity measurement system is corrected, the real-time carbon emission flow of the coal-fired power plant is obtained, and the velocity correction mode of the velocity field coefficient can be dynamically adjusted according to the real-time unit load through the dynamic velocity field model. And the accuracy of carbon emission flow measurement is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission measurement, and in particular to an online carbon emission monitoring method based on dynamically adjusting a velocity field coefficient of a unit load. Background Art

[0002] Continuous carbon dioxide monitoring technology involves selecting a uniform flow field in a straight section of a horizontal flue or vertical chimney where flue gas is discharged from a thermal power plant. The system then installs monitoring equipment such as a carbon dioxide analyzer, flow meter, temperature sensor, and hygrometer to measure parameters such as carbon dioxide concentration, flue gas flow rate, temperature, and humidity online to calculate the total amount of carbon dioxide emissions. The most influential parameters are carbon dioxide concentration and flue gas flow rate. Carbon dioxide concentration measurement technology is relatively mature, and given the relatively uniform distribution of carbon dioxide concentration in the flue of coal-fired power plants, the uncertainty can be controlled to a low level of 1% to 2%. However, unlike the concentration distribution, the flow field within the flue is less uniform. Even at high chimney heights, flow velocity still exhibits some non-uniformity. Furthermore, the flow field within the same cross-section varies significantly under different operating conditions, with even greater non-uniformity in a two-furnace-one-tube configuration compared to a single furnace-one-tube configuration.

[0003] In existing coal-fired power plant flue gas flow monitoring solutions, single-point Pitot tube flowmeters, matrix flowmeters, and ultrasonic flowmeters are commonly used flow measurement devices. These devices usually estimate the average flow velocity of the entire flue cross section based on the velocity field coefficient.

[0004] However, the velocity field coefficient is generally obtained by measuring and comparing the monitoring equipment every quarter, and the flow rate of the continuous emission monitoring system is calibrated and corrected using the reference method. The fixed velocity field coefficient obtained by comparing the existing point flow rate, line flow rate measurement device with the L-type Pitot tube or S-type Pitot tube remains unchanged throughout the quarter. Summary of the Invention

[0005] The present invention provides an online carbon emission monitoring method based on the dynamic adjustment of the velocity field coefficient of the unit load, which is used to solve the defect of inaccurate carbon emission measurement data of coal-fired power plants by fixing the velocity field coefficient in the prior art.

[0006] In a first aspect, the present invention provides a method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficients according to unit load, comprising: Receive real-time unit loads from coal-fired power plants; Inputting the real-time unit load into a dynamic velocity field coefficient model and outputting a target velocity field coefficient corresponding to the real-time unit load, wherein the dynamic velocity field coefficient model is pre-constructed based on unit load samples and velocity field coefficient samples, and the velocity field coefficient samples are obtained by comparing flow velocity data measured by a three-dimensional pitot tube under multiple sets of different load conditions with flow velocity data measured by an original flow velocity measurement device; Based on the target velocity field coefficient, the flow velocity measured by the flow velocity measurement system is corrected to obtain the real-time carbon emission flow of the coal-fired power plant.

[0007] According to the present invention, a method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficients based on unit load is provided, which, before receiving the real-time unit load of a coal-fired power plant, further comprises: Determine the unit load sample as an independent variable and the velocity field coefficient sample affected by the independent variable and containing uncertainty as a dependent variable; Performing visualization analysis, correlation analysis, and lag analysis on the independent variables and the dependent variables to construct a multivariate regression model; By comparing the flow rate data measured by the three-dimensional pitot tube under multiple groups of different load conditions with the flow rate data measured by the original flow rate measuring equipment, the coefficients of the multivariable regression model are calibrated to obtain the dynamic velocity field coefficient model.

[0008] According to the present invention, a method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficients of unit load is provided, wherein the visual analysis of the independent variables and the dependent variables includes: removing abnormal values ​​from the unit load sample and the velocity field coefficient sample, and filling missing values ​​to complete data preprocessing; Based on the unit load samples and velocity field coefficient samples after the data preprocessing, a scatter plot is drawn to achieve overall trend observation of the unit load and velocity field coefficient.

[0009] According to the present invention, a method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficient of unit load is provided, wherein the correlation analysis of the independent variable and the dependent variable comprises: determining a Pearson correlation coefficient between the unit load sample and the velocity field coefficient sample as a linear relationship; determining a mutual relationship among the unit load sample, the implicit variable, and the velocity field coefficient sample as a nonlinear relationship; The implicit variable includes at least one of flue gas temperature, flue gas pressure, flue gas humidity and flue gas oxygen content.

[0010] According to the present invention, a method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficients of unit load is provided, wherein the visualization analysis, correlation analysis and hysteresis analysis of the independent variables and the dependent variables are performed to construct a multivariate regression model, including: Determining a linear regression equation and a nonlinear regression equation based on the results of the visualization analysis, the correlation analysis, and the lag analysis; A multivariate regression model is constructed by combining the linear regression equation, the nonlinear regression equation and the correction coefficient.

[0011] According to the present invention, a method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficients based on unit load is provided. Before determining the unit load sample as an independent variable and the velocity field coefficient sample affected by the independent variable and containing uncertainty as a dependent variable, the method further includes: From the electronics room closest to the carbon emission online monitoring device, use optical fiber to connect the power plant's distributed control system to the carbon emission online monitoring system to collect different unit loads as unit load samples; The actual operating parameters corresponding to different load samples of the unit are determined, and the velocity field coefficients corresponding to the actual operating parameters are calculated as velocity field coefficient samples.

[0012] According to the present invention, a method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficients according to unit load is provided, wherein the velocity field coefficients corresponding to the actual operating parameters are calculated as velocity field coefficient samples, including: A reference experiment was carried out using a three-dimensional Pitot tube to obtain the velocity field coefficients corresponding to the actual operating parameters as velocity field coefficient samples.

[0013] According to the present invention, a method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficients according to unit load is provided. The method corrects the flow rate measured by the flow rate measurement system based on the target velocity field coefficient to obtain the real-time carbon emission flow of the coal-fired power plant, including: Performing a nonlinear grid-point weighted operation on the target velocity field coefficient and the flow velocity measured by the flow velocity measurement system to reconstruct an equivalent average flow velocity of the entire cross-section of the flue; The non-dispersive infrared absorption method is used synchronously to analyze the carbon dioxide concentration of the flue gas online, and finally the carbon emission flow rate is determined through the vector integral operation of the equivalent average flow velocity, the carbon dioxide concentration and the full cross section.

[0014] In a second aspect, the present invention further provides an online carbon emission monitoring device for dynamically adjusting the velocity field coefficient based on unit load, comprising: A receiving module, used for receiving real-time unit loads of coal-fired power plants; a big data module for inputting the real-time unit load into a dynamic velocity field coefficient model and outputting a target velocity field coefficient corresponding to the real-time unit load, wherein the dynamic velocity field coefficient model is pre-constructed based on unit load samples and velocity field coefficient samples, and the velocity field coefficient samples are obtained by comparing flow velocity data measured by a three-dimensional pitot tube under multiple sets of different load conditions with flow velocity data measured by an original flow velocity measurement device; The measurement module is used to correct the flow rate measured by the flow rate measurement system based on the target velocity field coefficient to obtain the real-time carbon emission flow of the coal-fired power plant.

[0015] In a third aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, an online carbon emission monitoring method for dynamically adjusting the velocity field coefficient based on unit load as described in any one of the above methods is implemented.

[0016] In a fourth aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an online carbon emission monitoring method based on dynamic adjustment of the velocity field coefficient based on unit load as described in any of the above.

[0017] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described methods for online monitoring of carbon emissions by dynamically adjusting the velocity field coefficient based on unit load.

[0018] The present invention provides an online carbon emission monitoring method based on dynamically adjusting the velocity field coefficient under unit load, comprising receiving the real-time unit load of a coal-fired power plant; inputting the real-time unit load into a dynamic velocity field coefficient model, and outputting a target velocity field coefficient corresponding to the real-time unit load, wherein the dynamic velocity field coefficient model is pre-constructed based on unit load samples and velocity field coefficient samples, and the velocity field coefficient samples are obtained by comparing the velocity data measured by a three-dimensional pitot tube under multiple groups of different load conditions with the velocity data measured by the original flow velocity measurement equipment; based on the target velocity field coefficient, the velocity measured by the velocity measurement system is corrected to obtain the real-time carbon emission flow of the coal-fired power plant, and the dynamic velocity field model can dynamically adjust the velocity field coefficient to correct the flow according to the real-time electromechanical load, thereby effectively improving the accuracy of the carbon emission flow measurement compared with the fixed velocity field coefficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 : is a flow chart of the method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficient of unit load provided in this embodiment; Figure 2 Schematic diagram of the structure of the carbon emission online monitoring device for dynamically adjusting the velocity field coefficient based on unit load provided in this embodiment; Figure 3 It is a structural diagram of the electronic device provided in this embodiment. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0022] Figure 1 It is a flow chart of the online carbon emission monitoring method based on the dynamic adjustment of the velocity field coefficient according to the unit load provided in this embodiment.

[0023] Currently, most point and line velocity flow measurement devices use fixed velocity field coefficients derived from an L-type or S-type pitot tube. L-type pitot tubes have smaller apertures and are prone to clogging when particulate matter concentrations are high. Therefore, they are rarely used in field measurements of flue gas from fixed pollution sources. S-type pitot tubes have larger pressure tapping openings and are less susceptible to clogging by particulate matter. While they are the most widely used method for measuring flue gas velocity, they can be inaccurate when the flue gas flows off-axis.

[0024] The traditional fixed velocity field coefficient method uses a fixed velocity field coefficient. The general S-type pitot tube coefficient is 0.84. The average velocity of the entire flue cross section is calculated by measuring the velocity at a single point or a small number of measuring points. When the load of the coal-fired unit changes, the flue gas flow velocity distribution will change significantly. However, most of the existing thermal power plants are frequently peak-shaving. When the unit load changes, the boiler combustion intensity, flue gas temperature / pressure, and flue resistance distribution will all change dynamically, resulting in increased non-uniformity in the flow velocity distribution. The flow field distribution is different under different loads and operating conditions, and the velocity field coefficient is difficult to fix. According to existing standards, the velocity field coefficient is measured once a quarter and is a fixed value during this period. It does not change with changes in the unit load and operating conditions. This also makes it impossible to accurately measure the flue gas flow velocity using representative point and representative line flow measurement devices such as pitot tubes and ultrasonic flowmeters.

[0025] Based on this, Figure 1 As shown, the method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficient of unit load provided by the embodiment of the present invention mainly includes the following steps: 101. Receive real-time unit loads of coal-fired power plants.

[0026] In a specific implementation, the online carbon emissions monitoring system collects flue gas physical parameters, including temperature, pressure, oxygen content, humidity, carbon dioxide concentration, and flue gas flow. The system uses built-in formulas to automatically calculate real-time carbon emissions data. From the electronics room closest to the online carbon emissions monitoring system, an optical fiber connects the power plant's DCS system to the online carbon emissions monitoring system, collecting real-time load signals from the coal-fired power plant's units.

[0027] 102. Input the real-time unit load into the dynamic velocity field coefficient model, and output the target velocity field coefficient corresponding to the real-time unit load. The dynamic velocity field coefficient model is pre-constructed based on the unit load sample and the velocity field coefficient sample. The velocity field coefficient sample is obtained by comparing the velocity data measured by the three-dimensional pitot tube under multiple groups of different load conditions with the velocity data measured by the original flow velocity measurement equipment.

[0028] After collecting the real-time unit load signal, it is input into the pre-built dynamic velocity field coefficient model. The model automatically outputs the corresponding target velocity field coefficient through internal calculation. The target velocity field coefficient output by the dynamic velocity field coefficient model corresponds to the real-time unit load.

[0029] The target velocity field coefficient obtained through the dynamic velocity field coefficient model is updated and changed at any time as the unit load changes, thereby ensuring that the obtained velocity field coefficient is more in line with the current operating conditions of the coal-fired unit and improving the accuracy of flow measurement.

[0030] Compared with the traditional S-type Pitot tube, the flow measurement method using a three-dimensional Pitot tube can effectively eliminate the influence of inaccurate measurement when the flue gas deviates from the axis flow, thereby ensuring the accuracy of the dynamic velocity field coefficient sample and further improving the accuracy of the measurement results.

[0031] 103. Based on the target velocity field coefficient, the flow velocity measured by the flow velocity measurement system is corrected to obtain the real-time carbon emission flow of the coal-fired power plant.

[0032] The dynamic target velocity field coefficient obtained is completely consistent with the current real-time unit load, thereby ensuring the dynamic adjustment characteristics of the target velocity field coefficient, and further correcting the flow rate measured by the flow rate measurement system, ensuring the accuracy of the flow rate, and thus ensuring the accuracy of the carbon emission flow of the coal-fired power plant.

[0033] Furthermore, based on the above embodiment, before receiving the real-time unit load of the coal-fired power plant, this embodiment also includes: determining the unit load sample as the independent variable and the velocity field coefficient sample affected by the independent variable and containing uncertainty as the dependent variable; performing visualization analysis, correlation analysis and lag analysis on the independent variable and the dependent variable to construct a multivariate regression model; calibrating the coefficients of the multivariate regression model by comparing the results of the three-dimensional Pitot tube measurements under multiple groups of different load conditions with the flow rate data measured by the original flow rate measurement equipment to obtain a dynamic velocity field coefficient model.

[0034] Specifically, the independent variable is defined as x, and the dependent variable can be defined as y. x is the observable input variable, the unit load, and y is subject to uncertainty, primarily in flue gas temperature, humidity, and pressure. Then, outliers are removed from the unit load and velocity field coefficient samples. These outliers include sensor failures and other causes. Missing values ​​are then filled using interpolation or time series-based forecasting methods to complete data preprocessing.

[0035] Data preprocessing includes outlier removal using the LOF (Local Outlier Factor) algorithm, with a neighborhood parameter of k set to 15, to identify and remove anomalous samples that deviate from the main distribution cluster. Missing value filling involves using the ARIMA model to predict time series data and fill missing values ​​in the unit load time series (e.g., recovering data from brief sensor failures), maintaining data continuity to support subsequent modeling.

[0036] Visual analysis of independent and dependent variables includes the creation of xy scatter plots based on pre-processed unit load and velocity field coefficient samples, enabling observation of overall trends in unit load and velocity field coefficients. Visual analysis also includes the development of a three-dimensional dynamic scatter plot matrix that simultaneously displays unit load time series curves, velocity field coefficient distribution histograms, and load-velocity field coefficient contour plots. A visual interactive interface can also be used to identify flow field distortion characteristics corresponding to load mutation points.

[0037] Correlation analysis of the independent and dependent variables includes determining the Pearson correlation coefficient between the unit load sample x and the velocity field coefficient sample y as a linear relationship, and determining the mutual correlation between the unit load sample x, the latent variable, and the velocity field coefficient sample y as a nonlinear relationship. The latent variable includes at least one of flue gas temperature, flue gas pressure, flue gas humidity, and flue gas oxygen content. The correlation analysis also includes using the Maximum Information Coefficient (MIC) to assess nonlinear relationships and identify complex nonlinear associations between unit load and velocity field coefficients (such as quadratic terms and interaction effects), thereby enhancing the model's explanatory power.

[0038] Perform hysteresis analysis on the independent and dependent variables, including examining whether y has a delayed response to changes in x. This also includes analyzing the time lag effects (e.g., delay time k) between load changes and velocity field responses through cross-wavelet transforms to capture the phase delay characteristics of the dynamic system.

[0039] Based on the results of visualization analysis, correlation analysis, and lag analysis, the linear regression equation and nonlinear regression equation are determined. The linear regression equation, nonlinear regression equation, and correction coefficient are combined to construct a multivariate regression model.

[0040] Furthermore, before determining the unit load samples as independent variables and the velocity field coefficient samples affected by the independent variables and containing uncertainty as dependent variables, this embodiment also includes: connecting the power plant distributed control system and the carbon emission online monitoring system with optical fiber from the electronic room closest to the carbon emission online monitoring device to collect different unit loads as unit load samples; determining the actual operating parameters corresponding to the different unit load samples, and calculating the velocity field coefficients corresponding to the actual operating parameters as velocity field coefficient samples. Calculating the velocity field coefficients corresponding to the actual operating parameters as velocity field coefficient samples includes: conducting a reference experiment using a three-dimensional pitot tube to obtain the velocity field coefficients corresponding to the actual operating parameters as velocity field coefficient samples.

[0041] Specifically, the carbon emission online monitoring system is used to collect the flue gas physical parameters under multiple loads (30%-100%) of the unit, including temperature, pressure, oxygen content, humidity, flue gas flow, etc., and the velocity field coefficient measured by the reference method. These parameters are used as unit load samples and velocity field coefficient samples and are brought into the model to obtain various correction coefficients.

[0042] The velocity distribution is then measured using a three-dimensional Pitot tube matrix, and the model coefficients are reverse-calibrated to achieve dynamic adaptive correction. By constructing a feedback control system, the system automatically adjusts the velocity field coefficients as the load changes, and accordingly corrects the measured flue gas flow rate. Continuous testing in real applications and further optimization of the model based on the results ensure accurate and reliable measurement results under various operating conditions.

[0043] Furthermore, in this embodiment, the flow rate measured by the flow rate measurement system is corrected based on the target velocity field coefficient to obtain the real-time carbon emission flow of the coal-fired power plant, including: performing a nonlinear grid weighted operation on the target velocity field coefficient and the flow rate measured by the flow rate measurement system to reconstruct the equivalent average flow velocity of the entire cross-section of the flue; synchronously using the non-dispersive infrared absorption method to analyze the flue gas carbon dioxide concentration online, and finally determining the carbon emission flow through the vector integral operation of the equivalent average flow velocity, carbon dioxide concentration and full cross-section.

[0044] Specifically, the dynamic correction method ensures the accuracy of the average flow velocity compared with the simple static multiplication method, and the measurement error is effectively reduced through the real-time velocity field coupling mechanism, thereby better ensuring the accuracy of carbon emission flow measurement.

[0045] This online carbon emissions monitoring method, which dynamically adjusts the velocity field coefficient based on unit load, eliminates the impact of inaccurate measurements caused by off-axis flue gas flow in manual reference tests by using an advanced three-dimensional pitot tube instead of a traditional S-shaped pitot tube. The velocity field coefficient dynamically matches load changes: As unit load changes, the model automatically adjusts the velocity field coefficient, eliminating errors caused by traditional fixed velocity field coefficient methods.

[0046] Furthermore, visualization analysis, correlation analysis, and lag analysis are performed on the independent and dependent variables to construct a multivariate regression model, which may also include: collection The sample data of each time period is defined, and the unit load sample is defined as , the speed coefficient sample is .

[0047] Visual analysis includes: drawing time series graphs of unit load samples and velocity field coefficient samples to observe their changing trends over time, and drawing scatter plots of unit load samples and velocity field coefficient samples to observe the relationship between the two.

[0048] Correlation analysis includes: the correlation coefficient between the computer group load sample and the velocity field coefficient sample through formula (1) ; (1) in, and are the means of velocity field coefficient samples and unit load samples, It is The unit load at a time point, It is The velocity field coefficient at a time point.

[0049] Lag analysis includes: calculation of different lag times k The correlation coefficient under , such as (2): (2) Then, based on the correlation coefficient between the unit load sample and the velocity field coefficient sample and different lag times k The correlation coefficient under , build a multivariate regression model to predict the velocity field coefficients. independent variables, 、 、 、 、…、 ,in is the unit load, and the others are factors that may affect the velocity field coefficient, including temperature, pressure, humidity, etc. The multivariate linear regression model is determined as (3): (3) It is The velocity field coefficient at each time point, is the intercept term, are the regression coefficients of the respective variables, is the error term.

[0050] Taking into account the influence of nonlinear factors, the optimization is (4): (4) Then, by introducing the interaction terms between unit load and other variables, we get (5): (5) Then use the least squares method to estimate the regression coefficient, as shown in formula (6): (6) in, is the design matrix, containing the observed values ​​of all independent variables, is the observed value vector of the velocity field coefficients.

[0051] Then, LASSO regression is used to select variables to avoid overfitting, as shown in (7): (7) Here, λ is the regularization parameter.

[0052] Finally, the model performance is evaluated by the mean square error, as shown in (8): (8) in, are the velocity field coefficients predicted by the model.

[0053] Through continuous optimization and evaluation of the multivariable regression model, it is ensured that the final regression model can more accurately predict the velocity field coefficient based on different unit loads, further improving the accuracy of the final carbon emissions measurement.

[0054] Based on the same general inventive concept, the present invention also protects an online carbon emission monitoring device based on the dynamic adjustment of the velocity field coefficient of the unit load. The online carbon emission monitoring device based on the dynamic adjustment of the velocity field coefficient of the unit load described below and the online carbon emission monitoring method based on the dynamic adjustment of the velocity field coefficient of the unit load described above can be referenced to each other.

[0055] Figure 2 It is a structural schematic diagram of the carbon emission online monitoring device provided in this embodiment that dynamically adjusts the velocity field coefficient based on the unit load.

[0056] like Figure 2 As shown, this embodiment provides an online carbon emission monitoring device for dynamically adjusting the velocity field coefficient based on unit load, comprising: Receiving module 201, used to receive real-time unit load of coal-fired power plants; Big data module 202 is used to input real-time unit load into a dynamic velocity field coefficient model and output a target velocity field coefficient corresponding to the real-time unit load. The dynamic velocity field coefficient model is pre-built based on unit load samples and velocity field coefficient samples. The velocity field coefficient samples are obtained by comparing flow rate data measured by a three-dimensional pitot tube under multiple sets of different load conditions with flow rate data measured by an original flow velocity measurement device. The measurement module 203 is configured to correct the flow velocity measured by the flow velocity measurement system based on the target velocity field coefficient to obtain the real-time carbon emission flow of the coal-fired power plant.

[0057] Figure 3 It is a structural diagram of the electronic device provided in this embodiment.

[0058] like Figure 3 As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communications bus 340, wherein the processor 310, the communications interface 320, and the memory 330 communicate with each other via the communications bus 340. The processor 310 may call logic instructions in the memory 330 to execute an online carbon emission monitoring method for dynamically adjusting the velocity field coefficient based on unit load. The method includes: receiving real-time unit load of a coal-fired power plant; inputting the real-time unit load into a dynamic velocity field coefficient model, and outputting a target velocity field coefficient corresponding to the real-time unit load, wherein the dynamic velocity field coefficient model is pre-constructed based on unit load samples and velocity field coefficient samples, wherein the velocity field coefficient samples are obtained by comparing flow velocity data measured by a three-dimensional pitot tube under multiple sets of different load conditions with flow velocity data measured by an original flow velocity measurement device; and based on the target velocity field coefficient, correcting the flow velocity measured by the flow velocity measurement system to obtain the real-time carbon emission flow of the coal-fired power plant.

[0059] Furthermore, the logic instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0060] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the online carbon emission monitoring method based on the dynamic adjustment of the velocity field coefficient by the unit load provided by the above methods. The method includes: receiving the real-time unit load of the coal-fired power plant; inputting the real-time unit load into the dynamic velocity field coefficient model, and outputting the target velocity field coefficient corresponding to the real-time unit load, wherein the dynamic velocity field coefficient model is pre-constructed based on the unit load sample and the velocity field coefficient sample, and the velocity field coefficient sample is obtained by comparing the flow rate data measured by the three-dimensional Pitot tube under multiple groups of different load conditions with the flow rate data measured by the original flow rate measurement equipment; based on the target velocity field coefficient, the flow rate measured by the flow rate measurement system is corrected to obtain the real-time carbon emission flow of the coal-fired power plant.

[0061] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the online carbon emission monitoring method based on the dynamic adjustment of the velocity field coefficient of the unit load provided by the above-mentioned methods, the method comprising: receiving the real-time unit load of a coal-fired power plant; inputting the real-time unit load into a dynamic velocity field coefficient model, and outputting a target velocity field coefficient corresponding to the real-time unit load, wherein the dynamic velocity field coefficient model is pre-constructed based on a unit load sample and a velocity field coefficient sample, and the velocity field coefficient sample is obtained by comparing the flow rate data measured by a three-dimensional pitot tube under multiple groups of different load conditions with the flow rate data measured by the original flow rate measurement equipment; based on the target velocity field coefficient, the flow rate measured by the flow rate measurement system is corrected to obtain the real-time carbon emission flow of the coal-fired power plant.

[0062] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0063] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficient of unit load, characterized in that: include: Receive real-time unit loads from coal-fired power plants; Input the real-time unit load to the dynamic velocity field coefficient model, and output the target velocity field coefficient corresponding to the real-time unit load, wherein the dynamic velocity field coefficient model is pre-constructed based on the unit load sample and the velocity field coefficient sample, and the velocity field coefficient sample is obtained by comparing the flow velocity data measured by the three-dimensional pitot tube under multiple groups of different load conditions with the flow velocity data measured by the original flow velocity measurement equipment; Based on the target velocity field coefficient, the flow velocity measured by the flow velocity measurement system is corrected to obtain the real-time carbon emission flow of the coal-fired power plant.

2. The method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficient of unit load according to claim 1 is characterized in that: Before receiving the real-time unit load of the coal-fired power plant, the method further includes: Determine the unit load sample as an independent variable, and the velocity field coefficient sample affected by the independent variable and containing uncertainty as a dependent variable; Performing visualization analysis, correlation analysis and lag analysis on the independent variables and the dependent variables to construct a multivariate regression model; By comparing the flow velocity data measured by the three-dimensional pitot tube under multiple groups of different load conditions with the flow velocity data measured by the original flow velocity measuring equipment, the coefficients of the multivariate regression model are calibrated to obtain the dynamic velocity field coefficient model.

3. The method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficient of unit load according to claim 2 is characterized in that: The visual analysis of the independent variable and the dependent variable includes: Removing abnormal values ​​from the unit load sample and the velocity field coefficient sample, and filling missing values ​​to complete data preprocessing; Based on the unit load samples and velocity field coefficient samples after the data preprocessing, a scatter plot is drawn to achieve overall trend observation of the unit load and velocity field coefficient.

4. The method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficient of unit load according to claim 2 is characterized in that: The performing correlation analysis on the independent variable and the dependent variable comprises: determining a Pearson correlation coefficient between the unit load sample and the velocity field coefficient sample as a linear relationship; Determining the mutual relationship between the unit load sample, the implicit variable and the velocity field coefficient sample as a nonlinear relationship; The implicit variable includes at least one of flue gas temperature, flue gas pressure, flue gas humidity and flue gas oxygen content.

5. The method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficient of unit load according to claim 2 is characterized in that: The performing of visualization analysis, correlation analysis and lag analysis on the independent variables and the dependent variables to construct a multivariate regression model includes: Determine a linear regression equation and a nonlinear regression equation based on the results of the visualization analysis, correlation analysis and lag analysis; A multivariate regression model is constructed by combining the linear regression equation, the nonlinear regression equation and the correction coefficient.

6. The method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficient of unit load according to claim 2 is characterized in that: Before determining the unit load sample as an independent variable and the velocity field coefficient sample affected by the independent variable and containing uncertainty as a dependent variable, the method further includes: From the electronic room closest to the carbon emission online monitoring device, use optical fiber to connect the power plant distributed control system and the carbon emission online monitoring system to collect different unit loads as unit load samples; The actual operating parameters corresponding to different load samples of the unit are determined, and the velocity field coefficients corresponding to the actual operating parameters are calculated as velocity field coefficient samples.

7. The method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficient of unit load according to claim 6 is characterized in that: The calculating the velocity field coefficient corresponding to the actual operating parameter as a velocity field coefficient sample includes: A reference experiment was carried out using a three-dimensional Pitot tube to obtain the velocity field coefficients corresponding to the actual operating parameters as velocity field coefficient samples.

8. The method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficient of unit load according to any one of claims 1 to 7, characterized in that: Based on the target velocity field coefficient, the flow velocity measured by the flow velocity measurement system is corrected to obtain the real-time carbon emission flow of the coal-fired power plant, including: The target velocity field coefficient is subjected to nonlinear grid-point weighted operation with the velocity measured by the velocity measurement system to reconstruct the equivalent average velocity of the entire cross section of the flue; The non-dispersive infrared absorption method is used synchronously to analyze the carbon dioxide concentration of the flue gas online, and finally the carbon emission flow rate is determined through the vector integral operation of the equivalent average flow rate, the carbon dioxide concentration and the full cross section.

9. An online carbon emission monitoring device based on dynamic adjustment of velocity field coefficient of unit load, characterized in that: include: A receiving module, used for receiving real-time unit loads of coal-fired power plants; A big data module, used for inputting the real-time unit load into a dynamic velocity field coefficient model, and outputting a target velocity field coefficient corresponding to the real-time unit load, wherein the dynamic velocity field coefficient model is pre-constructed based on a unit load sample and a velocity field coefficient sample, and the velocity field coefficient sample is obtained by comparing the velocity data measured by a three-dimensional pitot tube under multiple groups of different load conditions with the velocity data measured by an original velocity measurement device; The measurement module is used to correct the flow rate measured by the flow rate measurement system based on the target velocity field coefficient to obtain the real-time carbon emission flow of the coal-fired power plant.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for online monitoring of carbon emissions by dynamically adjusting the velocity field coefficient based on unit load as described in any one of claims 1 to 8 is implemented.

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