Online Monitoring Method for Carbon Emissions Based on Dynamically Adjusting the Speed Field Coefficient According to Unit Load

Abstract

The present invention provides an online monitoring method for carbon emissions based on dynamically adjusting velocity field coefficients under unit load, which relates to the technical field of carbon emission measurement, including receiving real-time unit loads of coal-fired power plants; inputting the real-time unit loads 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 actual measurements of three-dimensional pitot tubes under multiple groups of different load conditions with velocity data measured by original 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 unit load, thereby effectively improving the accuracy of carbon emission flow measurement compared with a fixed velocity field coefficient.

Description

Technical Field

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

[0002] The continuous carbon dioxide monitoring technology measures the total carbon dioxide emissions by calculating parameters such as carbon dioxide concentration, flue gas velocity, temperature, and humidity through arranging and installing monitoring devices such as carbon dioxide analyzers, flow meters, temperature sensors, and hygrometers in a straight pipe section with a uniform flow field in the horizontal flue or vertical chimney of the flue gas discharged from a thermal power plant. Among them, the most influential parameters are carbon dioxide concentration and flue gas velocity. The carbon dioxide concentration measurement technology is relatively mature, and coupled with the relatively uniform distribution of carbon dioxide concentration in the flue of coal-fired thermal power units, the uncertainty can be controlled at a relatively low level of 1% - 2%. Different from the concentration distribution, the flow field in the flue is poorly evenly distributed. Even at a relatively high height of the chimney, there is still a certain degree of unevenness in the flow velocity, and there are also significant differences in the flow field of the same cross-section under different working conditions. The unevenness of two furnaces sharing one pipe is stronger than that of one furnace with one pipe.

[0003] In the existing flue gas flow monitoring schemes of coal-fired power plants, single-point Pitot tube flow meters, matrix flow meters, and ultrasonic flow meters are commonly used flow measurement devices, and 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 calibrated and corrected for the flow velocity of the continuous emission monitoring system by comparing the measurement of the monitoring equipment every quarter using a reference method. The fixed velocity field coefficient obtained by comparing the existing point flow velocity and line flow velocity measurement devices combined with L-shaped Pitot tubes or S-shaped Pitot tubes remains unchanged throughout the quarter. Summary of the Invention

[0005] The present invention provides an online carbon emission monitoring method based on dynamically adjusting the velocity field coefficient according to the unit load to solve the defect in the prior art that the carbon emission measurement data of coal-fired power plants is inaccurate by using a fixed velocity field coefficient.

[0006] In a first aspect, the present invention provides an online carbon emission monitoring method based on dynamically adjusting the velocity field coefficient according to the unit load, including:

[0007] Receiving the real-time unit load of a coal-fired power plant;

[0008] Inputting the real-time unit load into a dynamic velocity field coefficient model to output a target velocity field coefficient corresponding to the real-time unit load, where 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 measured data of a three-dimensional Pitot tube with the measured velocity data of the original flow velocity measurement device under multiple different load conditions;

[0009] Based on the target velocity field coefficient, correct the flow velocity measured by the flow velocity measurement system to obtain the real-time carbon emission flow of the coal-fired power plant.

[0010] According to an online carbon emission monitoring method based on dynamically adjusting the velocity field coefficient according to the unit load provided by the present invention, before receiving the real-time unit load of the coal-fired power plant, it further includes:

[0011] Determine 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;

[0012] Perform visual analysis, correlation analysis and lag analysis on the independent variable and the dependent variable to construct a multivariable regression model;

[0013] Calibrate the coefficients of the multivariable regression model through the comparison results of the flow velocity data measured by the three-dimensional pitot tube and the original flow velocity measurement equipment under multiple different load conditions to obtain a dynamic velocity field coefficient model.

[0014] According to an online carbon emission monitoring method based on dynamically adjusting the velocity field coefficient according to the unit load provided by the present invention, the visual analysis of the independent variable and the dependent variable includes:

[0015] Remove the outliers in the unit load sample and the velocity field coefficient sample, and fill in the missing values to complete the data preprocessing;

[0016] Based on the unit load sample and the velocity field coefficient sample after the data preprocessing, draw a scatter plot to realize the overall trend observation of the unit load and the velocity field coefficient.

[0017] According to an online carbon emission monitoring method based on dynamically adjusting the velocity field coefficient according to the unit load provided by the present invention, the correlation analysis of the independent variable and the dependent variable includes:

[0018] Determine the Pearson correlation coefficient of the unit load sample and the velocity field coefficient sample as the linear relationship;

[0019] Determine the mutual relationship of the unit load sample, the latent variable and the velocity field coefficient sample as the non-linear relationship;

[0020] The latent variable includes at least one of flue gas temperature, flue gas pressure, flue gas humidity and flue gas oxygen content.

[0021] According to an online carbon emission monitoring method based on dynamically adjusting the velocity field coefficient according to the unit load provided by the present invention, the visual analysis, correlation analysis and lag analysis of the independent variable and the dependent variable to construct a multivariable regression model include:

[0022] Based on the results of the above-mentioned visual analysis, correlation analysis, and lag analysis, determine the linear regression equation and the non-linear regression equation;

[0023] Combine the linear regression equation, the non-linear regression equation, and the correction coefficient to construct a multivariate regression model.

[0024] According to a carbon emission online monitoring method based on dynamically adjusting the velocity field coefficient of unit load provided by the present invention, before determining the unit load sample as the independent variable and the velocity field coefficient sample affected by the independent variable and containing uncertainties as the dependent variable, it further includes:

[0025] Connect 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, and collect different unit loads as the unit load samples;

[0026] Determine the corresponding actual operating parameters under different unit load samples, and calculate the velocity field coefficient corresponding to the actual operating parameters as the velocity field coefficient sample.

[0027] According to a carbon emission online monitoring method based on dynamically adjusting the velocity field coefficient of unit load provided by the present invention, the calculating the velocity field coefficient corresponding to the actual operating parameters as the velocity field coefficient sample includes:

[0028] Conduct a reference experiment using a three-dimensional pitot tube to obtain the velocity field coefficient corresponding to the actual operating parameters as the velocity field coefficient sample.

[0029] According to a carbon emission online monitoring method based on dynamically adjusting the velocity field coefficient of unit load provided by the present invention, the correcting 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 rate of the coal-fired power plant includes:

[0030] Perform a non-linear 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 rate of the entire cross-section of the flue;

[0031] Simultaneously use the non-dispersive infrared absorption method to online analyze the carbon dioxide concentration in the flue gas, and finally determine the carbon emission flow rate through the vector integral operation of the equivalent average flow rate, the carbon dioxide concentration, and the entire cross-section.

[0032] In a second aspect, the present invention also provides a carbon emission online monitoring device based on dynamically adjusting the velocity field coefficient of unit load, including:

[0033] A receiving module, configured to receive the real-time unit load of the coal-fired power plant;

[0034] A big data module is configured to input the 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-constructed based on unit load samples and velocity field coefficient samples, and the velocity field coefficient samples are obtained by comparing the measured data of a three-dimensional Pitot tube with the measured data of the original flow velocity measuring device under multiple different load conditions.

[0035] A measurement module is configured to correct the flow velocity measured by a flow velocity measurement system based on the target velocity field coefficient to obtain the real-time carbon emission flow rate of a coal-fired power plant.

[0036] In a third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the online carbon emission monitoring method for dynamically adjusting the velocity field coefficient based on unit load as described in any one of the above.

[0037] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the online carbon emission monitoring method for dynamically adjusting the velocity field coefficient based on unit load as described in any one of the above.

[0038] In a fifth aspect, the present invention further provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the online carbon emission monitoring method for dynamically adjusting the velocity field coefficient based on unit load as described in any one of the above.

[0039] The online carbon emission monitoring method for dynamically adjusting the velocity field coefficient based on unit load provided by the present invention includes 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. 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 measured data of a three-dimensional Pitot tube with the measured data of the original flow velocity measuring device under multiple different load conditions; correcting the flow velocity measured by a flow velocity measurement system based on the target velocity field coefficient to obtain the real-time carbon emission flow rate of a coal-fired power plant. By means of the dynamic velocity field model, which can dynamically adjust the velocity field coefficient for flow velocity correction according to the real-time mechanical and electrical load, the accuracy of carbon emission flow rate measurement is effectively improved compared with the fixed velocity field coefficient. Description of the Drawings

[0040] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0041] Figure 1 is a schematic flow chart of the carbon emission online monitoring method based on dynamically adjusting the velocity field coefficient according to the unit load provided in this embodiment;

[0042] Figure 2 is a schematic structural diagram of the carbon emission online monitoring device based on dynamically adjusting the velocity field coefficient according to the unit load provided in this embodiment;

[0043] Figure 3 is a schematic structural diagram of the electronic device provided in this embodiment. Detailed implementation manners

[0044] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0045] Figure 1 is a schematic flow chart of the carbon emission online monitoring method based on dynamically adjusting the velocity field coefficient according to the unit load provided in this embodiment.

[0046] Currently, most point flow velocity and line flow velocity measurement devices obtain a fixed velocity field coefficient by comparing with an L-shaped Pitot tube or an S-shaped Pitot tube. The measuring holes of the L-shaped Pitot tube are relatively small and are easily blocked when the particulate matter concentration in the gas is relatively large. Therefore, it is less used in on-site measurements of flue gas from fixed pollution sources. The pressure measurement holes of the S-shaped Pitot tube have a larger opening and are not easily blocked by particulate matter. It is the most widely used method for measuring flue gas flow velocity at present. However, when the flue gas flows deviating from the axis, the flow velocity measured by the S-shaped Pitot tube is not accurate enough.

[0047] The traditional fixed velocity field coefficient method uses a fixed velocity field coefficient. Generally, the coefficient of the S-type Pitot tube is 0.84. The average velocity of the entire flue gas cross-section is estimated by measuring the flow velocity at a single point or a small number of measuring points. When the load of the coal-fired unit changes, the flue gas velocity distribution will change significantly. Most existing thermal power units have frequent peak shaving. When the unit load changes, the boiler combustion intensity, flue gas temperature / pressure, and flue gas resistance distribution will all change dynamically, resulting in an enhanced non-uniformity of the velocity distribution. The flow field distribution is different under different loads and operating conditions, and it is difficult to fix the velocity field coefficient. According to the existing standards, the velocity field coefficient is measured once every quarter and remains a fixed value during this period, without changing with the changes in the unit load and operating conditions. This will also cause the flow measurement devices using representative point methods and representative line methods such as Pitot tubes and ultrasonic flow meters to be unable to accurately measure the flue gas velocity.

[0048] Based on this, as Figure 1 shown, the online carbon emission monitoring method based on dynamically adjusting the velocity field coefficient according to the unit load provided by the embodiment of the present invention mainly includes the following steps:

[0049] 101. Receive the real-time unit load of the coal-fired power plant.

[0050] In a specific implementation process, through the online carbon emission monitoring system, flue gas physical property parameters are collected, including temperature, pressure, oxygen content, humidity, carbon dioxide concentration, flue gas flow rate, etc. The system has built-in calculation formulas to automatically calculate the real-time carbon emission data. From the electronic room closest to the online carbon emission monitoring system, the power plant DCS system is connected to the online carbon emission monitoring system with optical fibers to collect the unit load signal of the coal-fired power plant in real time.

[0051] 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. Among them, the dynamic velocity field coefficient model is pre-constructed based on the unit load samples and velocity field coefficient samples. The velocity field coefficient samples are obtained by comparing the measured flow velocity data of the three-dimensional Pitot tube under multiple different load conditions with the flow velocity data measured by the original flow velocity measuring equipment.

[0052] After the real-time unit load signal is collected, it is input into the pre-constructed dynamic velocity field coefficient model. After internal calculation by the model, the corresponding target velocity field coefficient is automatically output. The target velocity field coefficient output by the dynamic velocity field coefficient model corresponds to the real-time unit load.

[0053] The target velocity field coefficient obtained through the dynamic velocity field coefficient model changes and is updated at any time with different unit loads, so as to ensure that the obtained velocity field coefficient is more in line with the current operating conditions of the coal-fired unit, and improve the accuracy of flow measurement.

[0054] The method of actually measuring the flow rate using a three-dimensional Pitot tube effectively eliminates the inaccurate measurement caused by the deviation of the flue gas from the axis flow compared with the traditional S-shaped Pitot tube, thus ensuring the accuracy of the dynamic velocity field coefficient samples and further improving the accuracy of the measurement results.

[0055] 103. Based on the target velocity field coefficient, correct the flow velocity measured by the flow velocity measurement system to obtain the real-time carbon emission flow rate of the coal-fired power plant.

[0056] Through the obtained dynamic target velocity field coefficient, which is exactly corresponding to the current real-time unit load, the dynamic adjustment characteristic of the target velocity field coefficient is ensured. Further correct the flow velocity measured by the flow velocity measurement system, ensure the accuracy of the flow velocity, and then ensure the accuracy of the carbon emission flow rate of the coal-fired power plant.

[0057] Furthermore, on the basis of the above embodiments, before receiving the real-time unit load of the coal-fired power plant in this embodiment, it further includes: determining the unit load sample as the independent variable and the velocity field coefficient sample affected by the independent variable and containing uncertainties as the dependent variable; performing visual analysis, correlation analysis and lag analysis on the independent variable and the dependent variable to construct a multi-variable regression model; calibrating the coefficients of the multi-variable regression model through the comparison results of the measured values of the three-dimensional Pitot tube and the measured flow velocity data of the original flow velocity measuring equipment under multiple different load conditions to obtain a dynamic velocity field coefficient model.

[0058] Specifically, the independent variable is defined as x, and the dependent variable can be defined as y. x is the observable input variable of the unit load, and the sources of uncertainties of y mainly include the temperature, humidity and pressure of the flue gas, etc. Then, remove the outliers in the unit load sample and the velocity field coefficient sample. The outliers include the outliers caused by sensor failures or other reasons. And fill in the missing values, using the interpolation method or the prediction method based on time series for filling to complete the data preprocessing.

[0059] Among them, for the outlier removal in the data preprocessing, the LOF (Local Outlier Factor) algorithm is applied, and the neighborhood parameter k = 15 is set to identify and remove the abnormal samples deviating from the main distribution cluster. The missing value filling includes using the ARIMA model to predict and fill the missing values in the unit load time series for the time series data (such as data recovery when the sensor fails briefly) to maintain the data continuity to support subsequent modeling.

[0060] Among them, visual analysis is performed on the independent variable and the dependent variable, including: based on the unit load samples and velocity field coefficient samples after data preprocessing, an x-y scatter plot is drawn to observe the overall trend of the unit load and the velocity field coefficient. The visual analysis also includes: developing a three-dimensional dynamic scatter plot matrix to synchronously display the time-series change curve of the unit load, the distribution histogram of the velocity field coefficient, and the load-velocity field coefficient contour map, and the flow field distortion characteristics corresponding to the load mutation points can also be identified through the visual interaction interface.

[0061] Correlation analysis is performed on the independent variable and the dependent variable, including: determining the Pearson correlation coefficient of the unit load sample x and the velocity field coefficient sample y as the linear relationship; determining the mutual relationship among the unit load sample x, the latent variable, and the velocity field coefficient sample y as the non-linear 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 evaluate the non-linear relationship and identifying the complex non-linear associations (such as quadratic terms, interaction effects) between the unit load and the velocity field coefficient to improve the model interpretability.

[0062] Lag analysis is performed on the independent variable and the dependent variable, including: checking whether y has a delayed response to the change of x. It also includes: analyzing the time-delay effect (such as the delay time k) of the load change and the velocity field response through cross-wavelet transform to capture the phase-delay characteristics of the dynamic system.

[0063] Based on the results of the visual analysis, correlation analysis, and lag analysis, a linear regression equation and a non-linear regression equation are determined. Combining the linear regression equation, non-linear regression equation, and correction coefficient, a multi-variable regression model is constructed.

[0064] Furthermore, in this embodiment, before determining that the unit load sample is the independent variable and the velocity field coefficient sample that is affected by the independent variable and contains uncertainties is the dependent variable, it also includes: connecting the power plant distributed control system and the carbon emission online monitoring system with optical fibers from the electronic room closest to the carbon emission online monitoring device, collecting different unit loads as the unit load samples; determining the corresponding actual operating parameters under different unit load samples, and calculating the velocity field coefficient corresponding to the actual operating parameters as the velocity field coefficient sample. And calculating the velocity field coefficient corresponding to the actual operating parameters as the velocity field coefficient sample includes: performing a reference experiment using a three-dimensional Pitot tube to obtain the velocity field coefficient corresponding to the actual operating parameters as the velocity field coefficient sample.

[0065] Specifically, through the carbon emission online monitoring system, the flue gas physical property parameters (including temperature, pressure, oxygen content, humidity, flue gas flow, etc.) under multiple unit loads (30%-100%) of the unit are collected, and the velocity field coefficient measured by the reference method are used as the unit load samples and the velocity field coefficient samples, and are brought into the model to obtain each correction coefficient.

[0066] Then, the flow velocity distribution is actually measured through a three-dimensional Pitot tube matrix, and the model coefficients are inversely calibrated to achieve dynamic adaptive correction. By constructing a feedback control system, when the load changes, the system can automatically adjust the velocity field coefficients and accordingly correct the measured value of the flue gas flow rate. Continuously test in actual applications and further optimize the model according to the results to ensure that it can provide accurate and reliable measurement results under various working conditions.

[0067] Furthermore, based on the target velocity field coefficients in this embodiment, the flow velocity measured by the flow velocity measurement system is corrected to obtain the real-time carbon emission flow rate of the coal-fired power plant, including: performing a non-linear grid weighting operation on the target velocity field coefficients and the flow velocity measured by the flow velocity measurement system to reconstruct the equivalent average flow velocity of the entire cross-section of the flue; simultaneously using the non-dispersive infrared absorption method to online analyze the carbon dioxide concentration in the flue gas, and finally determining the carbon emission flow rate through the vector integral operation of the equivalent average flow velocity, carbon dioxide concentration, and the entire cross-section.

[0068] Specifically, through the dynamic correction method, compared with the simple static multiplication method, the accuracy of the average flow velocity is ensured, and the measurement error is effectively reduced through the real-time velocity field coupling mechanism, thereby better ensuring the accuracy of the carbon emission flow rate measurement.

[0069] In the online carbon emission monitoring method based on dynamically adjusting the velocity field coefficients according to the unit load of the present invention, in the reference test for manually measuring the velocity field coefficients, since an advanced three-dimensional Pitot tube is used instead of the traditional S-type Pitot tube for the reference test, the influence of inaccurate measurement when the flue gas deviates from the axis flow is eliminated. The velocity field coefficients are dynamically matched with the load changes: when the unit load changes, the model automatically adjusts the velocity field coefficients to eliminate the errors caused by the fixed velocity field coefficients in the traditional method.

[0070] Furthermore, performing visual analysis, correlation analysis, and lag analysis on independent variables and dependent variables to construct a multivariable regression model may further include:

[0071] Collect sample data for a period of time, define the samples, define the unit load samples as , and the velocity coefficient samples as .

[0072] The visual analysis includes: plotting the time series diagrams of the unit load samples and the velocity field coefficient samples to observe their changing trends over time, and plotting the scatter diagrams of the unit load samples and the velocity field coefficient samples to observe the relationship between the two.

[0073] The correlation analysis includes: calculating the correlation coefficient between the unit load samples and the velocity field coefficient samples through formula (1);

[0074] (1)

[0075] Among them, and are the means of the velocity field coefficient samples and the unit load samples respectively, is the unit load at the - th time point, is the velocity field coefficient at the - th time point.

[0076] The lag analysis includes: calculating the correlation coefficient k at different lag times , as in (2):

[0077] (2)

[0078] Then, based on the correlation coefficient between the unit load sample and the velocity field coefficient sample and the correlation coefficient k at different lag times , a multivariable regression model is constructed to predict the velocity field coefficient. Define independent variables, , , , , …, , where is the unit load, and the others are factors that may affect the velocity field coefficient, including temperature, pressure, humidity, etc. Determine the multivariable linear regression model as (3):

[0079] (3)

[0080] is the velocity field coefficient at the - th time point, is the intercept term, are the regression coefficients of the respective independent variables, is the error term.

[0081] Considering the influence of non - linear factors, it is optimized to (4):

[0082] (4)

[0083] Then introduce the interaction terms between the unit load and other variables to get (5):

[0084] (5)

[0085] Then use the least - squares method to estimate the regression coefficients, as in formula (6):

[0086] (6)

[0087] Among them, is the design matrix, which contains the observed values of all independent variables, is the vector of observed values of the velocity field coefficients.

[0088] Then, variable selection is carried out through LASSO regression to avoid overfitting, as shown in (7):

[0089] (7)

[0090] Among them, λ is the regularization parameter.

[0091] Finally, the performance of the model is evaluated through the mean squared error, as shown in (8):

[0092] (8)

[0093] Among them, is the velocity field coefficient predicted by the model.

[0094] Through continuous optimization and evaluation of the multivariate regression model, it is ensured that the finally obtained regression model can more accurately predict the velocity field coefficient based on different unit loads, further improving the accuracy of the final carbon emission measurement.

[0095] Based on the same general inventive concept, the present invention also protects an online carbon emission monitoring device for dynamically adjusting the velocity field coefficient based on the unit load. The online carbon emission monitoring device for dynamically adjusting the velocity field coefficient based on the unit load described below can be correspondingly referred to the online carbon emission monitoring method for dynamically adjusting the velocity field coefficient based on the unit load described above.

[0096] Figure 2 is the structural schematic diagram of the online carbon emission monitoring device for dynamically adjusting the velocity field coefficient based on the unit load provided in this embodiment.

[0097] As Figure 2 shown, an online carbon emission monitoring device for dynamically adjusting the velocity field coefficient based on the unit load provided in this embodiment includes:

[0098] A receiving module 201 for receiving the real-time unit load of the coal-fired power plant;

[0099] A big data module 202 for 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. Among them, the dynamic velocity field coefficient model is pre-constructed based on the unit load samples and the velocity field coefficient samples, and the velocity field coefficient samples are obtained by comparing the measured flow velocity data of the three-dimensional pitot tube with the original flow velocity measuring equipment under multiple different load conditions;

[0100] The measurement module 203 is configured 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 rate of the coal-fired power plant.

[0101] Figure 3 It is a schematic structural diagram of the electronic device provided in this embodiment.

[0102] As Figure 3 shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330, and a communication bus 340. Among them, the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340. The processor 310 can call the logical instructions in the memory 330 to execute the online carbon emission monitoring method for dynamically adjusting the velocity field coefficient based on the unit load. 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. The dynamic velocity field coefficient model is pre-constructed based on the unit load samples and the velocity field coefficient samples, and the velocity field coefficient samples are obtained by comparing the measured data of the three-dimensional pitot tube with the original flow rate measurement equipment under multiple different load conditions; based on the target velocity field coefficient, correct the flow rate measured by the flow rate measurement system to obtain the real-time carbon emission flow rate of the coal-fired power plant.

[0103] In addition, when the logical instructions in the above-mentioned memory 330 are implemented in the form of software function units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0104] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program 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 dynamically adjusting the velocity field coefficient according to the unit load provided by the above-mentioned various methods. The method includes: 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 flow velocity data measured by a three-dimensional pitot tube under multiple different load conditions with the flow velocity data measured by the original flow velocity measurement device; 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.

[0105] On another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it realizes the online carbon emission monitoring method based on dynamically adjusting the velocity field coefficient according to the unit load provided by the above-mentioned various methods. The method includes: 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 flow velocity data measured by a three-dimensional pitot tube under multiple different load conditions with the flow velocity data measured by the original flow velocity measurement device; 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.

[0106] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative labor.

[0107] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part 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, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0108] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the 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: 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; Visual analysis, correlation analysis and hysteresis analysis are performed on the independent variables and the dependent variables to construct a multivariate regression model; the coefficients of the multivariate regression model are calibrated 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 to obtain a dynamic velocity field coefficient model; 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: 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.

3. 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: 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.

4. 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: 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.

5. 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 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.

6. The method for online monitoring of carbon emissions based on dynamic adjustment of velocity field coefficient of unit load according to claim 5 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.

7. 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 6, 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.

8. An online carbon emission monitoring device based on dynamic adjustment of velocity field coefficient of unit load, characterized in that: include: A construction module is used to 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; perform visualization analysis, correlation analysis and hysteresis analysis on the independent variable and the dependent variable to construct a multivariate regression model; calibrate the coefficients of the 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 measurement equipment, and obtain a dynamic velocity field coefficient model; 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.

9. 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 7 is implemented.

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