Fixed emission source CO2 monitoring method based on adaptive compensation model

Abstract

The invention provides a fixed emission source CO2 monitoring method based on an adaptive compensation model. The fixed emission source CO2 monitoring method comprises the following steps: step S1, acquiring mixed gas of various CO2 concentrations; s2, collecting temperature data and humidity data of each mixed gas; s3, establishing a correction coefficient regression model corresponding to each CO2 concentration, and homogenizing each correction coefficient regression model to obtain a first correction coefficient; or controlling the infrared flue gas analyzer to establish a correction coefficient linear model corresponding to each CO2 concentration based on the temperature data and the humidity data, and homogenizing each correction coefficient linear model to obtain a second correction coefficient; and S4, acquiring a first CO2 monitoring value monitored by the electrochemical flue gas analyzer at the current moment or a second CO2 monitoring value monitored by the electrochemical flue gas analyzer, and combining the first correction coefficient or the second correction coefficient to obtain a corresponding CO2 monitoring accurate value. The method has the beneficial effect that the accuracy of CO2 flow monitoring can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of emission source monitoring, and in particular to a CO2 monitoring method for a stationary emission source based on an adaptive compensation model. Background Art

[0002] Because current continuous carbon emissions monitoring systems suffer from the challenges of extensive communication cabling, poor data transmission reliability, and high failure rates, numerous studies have proposed specific solutions to these issues. To address the challenges inherent in traditional thermal power plant configurations, where independent data acquisition devices transmit data to environmental regulators, resulting in extensive cabling, unstable data transmission, frequent equipment failures, and burdensome data management and maintenance, a continuous carbon emissions monitoring system has been developed. This solution reduces the number of communication interfaces required for environmental regulators and power grid agencies, optimizes data acquisition hardware configuration, reduces cabling usage and its complexity, and enhances overall system stability by implementing redundant core switches and data transmission loops to prevent data loss. Furthermore, the system integrates spectral absorption, information, sensor, and electronic technologies and applies them to CO2 emissions. Leveraging multi-sensor data integration, the system has developed a method for real-time monitoring of CO2 concentrations and created software to simulate environmental physical conditions. The laboratory-developed real-time CO2 monitoring mechanism has demonstrated its effectiveness in designing experimental procedures and collecting data. The technology not only provides precise measurements but also exhibits excellent real-time responsiveness, enabling rapid and accurate continuous tracking of CO2 concentrations.

[0003] At present, the CO2 concentration monitoring of fixed emission sources in enterprises generally adopts extractive sampling, that is, by extracting smoke samples from the chimney or exhaust pipe, and after necessary preliminary processing, the samples are input into the detection device for measurement. However, the flue gas measurement conditions analyzed by extractive sampling are different from the actual flue environment. The measurement results are difficult to accurately express the exact content of each component in the flue gas, and the concentration at different locations in the flue cannot be accurately measured. In addition, the flue gas of the emission enterprise flue, especially the flue gas outlet of coal-fired related enterprises and other fixed emission sources has the characteristics of high temperature and high humidity, which often has a relatively large impact on the measurement of the CO2 concentration of the flue gas component. As for the measurement of flue gas flow, the flue gas flow monitoring method currently used by most coal-fired power plants is to monitor the flue gas flow by a multi-point matrix flowmeter. However, the measured values ​​of the multi-point matrix flowmeter often deviate from the actual values, and there is a lack of correction means, which affects the accuracy of CO2 flow monitoring. Summary of the Invention

[0004] The technical problem to be solved by the present invention is how to improve the accuracy of CO2 flow monitoring. In order to overcome the defects of the above-mentioned existing technologies (or related technologies), the present invention provides a CO2 monitoring method for fixed emission sources based on an adaptive compensation model.

[0005] The present invention provides a CO2 monitoring method for a stationary emission source based on an adaptive compensation model. A gas source module, a temperature and humidity control module, and a flue gas monitoring module are pre-built and connected in sequence. The gas source module is used to collect N2 gas and CO2 gas to obtain a mixed gas. The temperature and humidity control module is used to heat or humidify the mixed gas. The flue gas monitoring module includes a thermometer, a hygrometer, an electrochemical flue gas analyzer, or an infrared flue gas analyzer. The CO2 monitoring method for a stationary emission source includes the following steps:

[0006] Step S1, controlling the gas source module to adjust and obtain the mixed gas with various CO2 concentrations;

[0007] Step S2, controlling the thermometer to collect temperature data of each of the mixed gases, and controlling the hygrometer to collect humidity data of each of the mixed gases;

[0008] Step S3, controlling the electrochemical flue gas analyzer to establish a correction coefficient regression model corresponding to each CO2 concentration based on the temperature data and the humidity data, and performing a averaging process on each correction coefficient regression model to obtain a first correction coefficient; or

[0009] Controlling the infrared flue gas analyzer to establish a correction coefficient linear model corresponding to each CO2 concentration based on the temperature data and the humidity data, and performing a homogenization process on each correction coefficient linear model to obtain a second correction coefficient;

[0010] Step S4, obtaining the first CO2 monitoring value or the second CO2 monitoring value obtained by the electrochemical flue gas analyzer at the current moment, and combining the first correction coefficient or the second correction coefficient to obtain the corresponding CO2 monitoring accurate value.

[0011] Compared with the prior art, the CO2 monitoring method for stationary emission sources based on the adaptive compensation model of the present invention has the following advantages:

[0012] In the present invention, mixed gas is collected through step S1, temperature and humidity data are collected through step S2, correction coefficients are analyzed through step S3, and correction calculation of the CO2 monitoring accurate value is performed through step S4. Through the series design of gas source module-temperature and humidity control module-flue gas monitoring module, the actual flue environment is accurately simulated, and a compensation model suitable for real working conditions, namely a correction coefficient regression model and a correction coefficient linear model, is established to solve the measurement distortion problem caused by environmental changes in traditional extractive sampling. At the same time, correction coefficient regression / linear models are constructed for two mainstream analyzers, electrochemical flue gas analyzer / infrared flue gas analyzer, respectively, which significantly improves the monitoring compatibility of equipment with different principles, and the first CO2 monitoring value and the second CO2 monitoring value are corrected by the first correction coefficient and the second correction coefficient respectively to obtain a CO2 monitoring accurate value closer to the actual value, which can greatly improve the accuracy of CO2 flow monitoring.

[0013] In one possible embodiment, the gas source module includes a first gas pressure reducing valve and a second gas pressure reducing valve, and the first gas pressure reducing valve and the second gas pressure reducing valve are respectively connected to the temperature and humidity control module through gas pipes, the first gas pressure reducing valve is used to collect the N2 gas and adjust the concentration ratio of the N2 gas in the mixed gas, and the second gas pressure reducing valve is used to collect the CO2 gas and adjust the concentration ratio of the CO2 gas in the mixed gas.

[0014] Compared with the existing technology, the above technical solution can achieve zoned control and regulation of N2 gas and CO2 gas through the first gas pressure reducing valve and the second gas pressure reducing valve, ensuring the stability of the mixed gas flow rate and eliminating the impact of flow fluctuations on the temperature and humidity control module.

[0015] In a possible embodiment, the flue gas monitoring module includes a flue gas duct, the thermometer, the hygrometer and a flue gas analyzer, the flue gas analyzer is the electrochemical flue gas analyzer or the infrared flue gas analyzer, one end of the flue gas duct is connected to the temperature and humidity control module, and the other end of the flue gas duct is connected to the outdoors, the thermometer and the hygrometer are arranged on the inner wall of the flue gas duct, and the flue gas analyzer is arranged on the outside of the flue gas duct and is connected to the inside of the flue gas duct.

[0016] Compared with the existing technology, the above technical solution can avoid the temperature and humidity attenuation error caused by external sampling through the embedded thermometer and hygrometer. At the same time, the flue gas analyzer is external but directly connected to the flue gas duct, which can prevent the flue gas analyzer from being damaged by high temperature and high humidity environment.

[0017] In one possible embodiment, the CO2 concentrations are 16.32%, 15.57%, 12.21%, 12.48%, 19.26%, 17.97%, 15.78%, and 13.07%, respectively. Then, in step S3, the correction coefficient regression model corresponding to the CO2 concentrations of 16.32%, 15.57%, 12.21%, and 12.48% is established; or

[0018] The correction coefficient linear model corresponding to the CO2 concentration of 19.26%, 17.97%, 15.78% and 13.07% was established.

[0019] Compared with the existing technology, the above technical solution can correspond to the actual CO2 emission concentration of coal-fired power plants through 8 groups of concentration values, ensuring the correction accuracy of the correction coefficient linear model and the correction coefficient regression model in the key range.

[0020] In a possible implementation, in step S3, each correction coefficient regression model is obtained by the following calculation formula:

[0021]

[0022] Wherein, y1 represents the correction coefficient regression model when the CO2 concentration is 16.32%; a1 represents the first regression coefficient; represents the temperature data; b1 represents the second regression coefficient; T represents the humidity data; c1 represents the third regression coefficient; y2 represents the correction coefficient regression model when the CO2 concentration is 15.57%; d1 represents the fourth regression coefficient; e1 represents the fifth regression coefficient; f1 represents the sixth regression coefficient; y3 represents the correction coefficient regression model when the CO2 concentration is 12.21%; g1 represents the seventh regression coefficient; h1 represents the eighth regression coefficient; i1 represents the ninth regression coefficient; y4 represents the correction coefficient regression model when the CO2 concentration is 12.48%; j1 represents the tenth regression coefficient; k1 represents the eleventh regression coefficient; l1 represents the twelfth regression coefficient.

[0023] Compared with the existing technology, the above technical solution can solve the signal transition mutation when the temperature is 60°C or the humidity is 50% through a segmented model, which can effectively improve the accuracy compared with a single model.

[0024] In a possible implementation, in step S3, the linear model of each correction coefficient is obtained by the following calculation formula:

[0025]

[0026] Wherein, y5 represents the correction coefficient linear model when the CO2 concentration is 19.26%; a2 represents the first linear coefficient; Represents the temperature data; b2 represents the second linear coefficient; T represents the humidity data; c2 represents the third linear coefficient; y6 represents the correction coefficient linear model when the CO2 concentration is 17.97%; d2 represents the fourth linear coefficient; e2 represents the fifth linear coefficient; f2 represents the sixth linear coefficient; y7 represents the correction coefficient linear model when the CO2 concentration is 15.78%; g2 represents the seventh linear coefficient; h2 represents the eighth linear coefficient; i2 represents the ninth linear coefficient; y8 represents the correction coefficient linear model when the CO2 concentration is 13.07%; j2 represents the tenth linear coefficient; k2 represents the eleventh linear coefficient; l2 represents the twelfth linear coefficient.

[0027] In a possible implementation, in step S3, the value range of the first regression coefficient is 0.00335–0.00337, the value range of the second regression coefficient is 0.00302–0.00304, the value range of the third regression coefficient is 0.99266–0.99463, the value range of the fourth regression coefficient is 0.00262–0.00264, the value range of the fifth regression coefficient is 0.00521–0.00524, and the value range of the sixth regression coefficient is 0.50130 –0.50252, the value range of the seventh regression coefficient is 0.00220–0.00222, the value range of the eighth regression coefficient is 0.00266–0.00269, the value range of the ninth regression coefficient is 1.25804–1.25986, the value range of the tenth regression coefficient is 0.00254–0.00257, the value range of the eleventh regression coefficient is 0.00509–0.00512, and the value range of the twelfth regression coefficient is 0.77078–0.77328.

[0028] In a possible implementation, in step S3, the first correction coefficient is obtained by the following calculation formula:

[0029]

[0030] Wherein, Y1 represents the first correction coefficient, represents the temperature data, and T represents the humidity data.

[0031] Compared with the existing technology, the above technical solution can eliminate the overfitting risk of the single concentration model by averaging four sets of correction coefficient regression models.

[0032] In a possible implementation, in step S3, the value of the first linear coefficient is 0.00135, the value of the second linear coefficient is 0.00202, the value of the third linear coefficient is 1.02017, the value of the fourth linear coefficient is 0.00133, the value of the fifth linear coefficient is 0.00204, the value of the sixth linear coefficient is 1.01996, the value of the seventh linear coefficient is 0.00133, the value of the eighth linear coefficient is 0.00203, the value of the ninth linear coefficient is 1.02136, the value of the tenth linear coefficient is 0.00134, the value of the eleventh linear coefficient is 0.00203, and the value of the twelfth linear coefficient is 1.02235.

[0033] In a possible implementation, in step S3, the second correction coefficient is obtained by the following calculation formula:

[0034]

[0035] Wherein, Y2 represents the second correction coefficient, represents the temperature data, and T represents the humidity data.

[0036] Compared with the existing technology, the above technical solution can simplify the engineering implementation by directly assigning linear coefficients, and is suitable for rapid on-site deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flow chart of the steps of the present invention;

[0038] Figure 2 This is a schematic diagram of the connection between the air source module, the temperature and humidity control module, and the flue gas monitoring module of the present invention;

[0039] Figure 3 Schematic diagram showing the comparison of CO2 monitoring values ​​of the electrochemical flue gas analyzer under different temperatures and humidities of the present invention;

[0040] Figure 4 This is a first correction coefficient fitting diagram under different temperatures and humidities of the present invention;

[0041] Figure 5 Schematic diagram showing the comparison of CO2 monitoring values ​​of the infrared flue gas analyzer under different temperatures and humidities of the present invention;

[0042] Figure 6 This is a fitting diagram of the second correction coefficient under different temperatures and humidities of the present invention. DETAILED DESCRIPTION

[0043] First, those skilled in the art should understand that these embodiments are merely for explaining the technical principles of the embodiments of the present invention and are not intended to limit the scope of protection of the embodiments of the present invention. Those skilled in the art may make adjustments as needed to adapt to specific application scenarios.

[0044] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] See also Figure 1 and Figure 2 The embodiment of the present invention discloses a CO2 monitoring method for a stationary emission source based on an adaptive compensation model. A gas source module, a temperature and humidity control module, and a flue gas monitoring module are pre-built and connected in sequence. The gas source module is used to collect N2 gas and CO2 gas to obtain a mixed gas. The temperature and humidity control module is used to heat or humidify the mixed gas. The flue gas monitoring module includes a thermometer, a hygrometer, an electrochemical flue gas analyzer, or an infrared flue gas analyzer. The CO2 monitoring method for a stationary emission source includes the following steps:

[0046] Step S1, controlling the gas source module to adjust and obtain mixed gases with various CO2 concentrations;

[0047] Step S2, controlling the thermometer to collect temperature data of each mixed gas, and controlling the hygrometer to collect humidity data of each mixed gas;

[0048] Step S3, controlling the electrochemical flue gas analyzer to establish a correction coefficient regression model corresponding to each CO2 concentration based on the temperature data and the humidity data, and performing a homogenization process on each correction coefficient regression model to obtain a first correction coefficient; or

[0049] Control the infrared flue gas analyzer to establish a linear model of correction coefficients corresponding to each CO2 concentration based on temperature data and humidity data, and perform averaging on each correction coefficient linear model to obtain a second correction coefficient;

[0050] Step S4, obtaining the first CO2 monitoring value or the second CO2 monitoring value obtained by the electrochemical flue gas analyzer at the current moment, and combining the first correction coefficient or the second correction coefficient to obtain the corresponding CO2 monitoring accurate value.

[0051] In an embodiment of the present invention, the gas source module includes a first gas pressure reducing valve and a second gas pressure reducing valve, and the first gas pressure reducing valve and the second gas pressure reducing valve are respectively connected to the temperature and humidity control module through gas pipes. The first gas pressure reducing valve is used to collect N2 gas and adjust the concentration ratio of N2 gas in the mixed gas. The second gas pressure reducing valve is used to collect CO2 gas and adjust the concentration ratio of CO2 gas in the mixed gas. The first gas pressure reducing valve adopts YQD-731L nitrogen gas pressure reducing valve, and the second gas pressure reducing valve adopts YQT-731L carbon dioxide gas pressure reducing valve. The measuring range of the second gas pressure reducing valve is 0-25MPa, the rated outlet pressure is 0.25Mpa, and the nominal flow rate is 25L / min. The measuring range of the first gas pressure reducing valve is 0-25Mpa, the rated outlet pressure is 0.3Mpa, and the nominal flow rate is 25L / min.

[0052] In the embodiment of the present invention, the temperature and humidity control module is composed of a gas heating device and a gas humidifying device, and the flue gas monitoring module is composed of a flue gas pipe, a thermometer, a hygrometer and a flue gas analyzer. The outer diameter of the flue gas pipe is about 60 mm, the inner diameter is about 58 mm, and the effective length is 1.5 m. Its function is to make the various gases mixed more evenly. A temperature and humidity sensor and a flue gas monitoring port are provided at the upper end of the flue gas pipe. The temperature measurement range is -40°C to +120°C, the humidity measurement range is 0%RH-100%RH, the temperature measurement accuracy is ±0.5°C, and the humidity measurement accuracy is ±3%RH. The thermometer and hygrometer mainly measure the temperature data and humidity data of the mixed gas in the flue gas pipe. The flue gas analyzer adopts an electrochemical flue gas analyzer and There are two common types of infrared flue gas analyzers, which mainly monitor the CO2 concentration in the mixed gas in the flue gas duct. Among them, the electrochemical flue gas analyzer adopts the JCY-80B electrochemical flue gas comprehensive analyzer, which uses the positioning electrolysis method to determine the harmful gases in the flue gas duct. The temperature range of the measured mixed gas is 0-500℃, the CO2 concentration measurement range is 0-20%, and the resolution is 0.01%; the infrared flue gas analyzer adopts the Keynes MS400 carbon dioxide detector, which realizes continuous monitoring of mixed gases of different concentrations based on the Lambert-Beer absorption law of gas absorption of infrared. The temperature range of the measured carbon dioxide and gas is 0-150℃, the CO2 concentration measurement range is 0-100%, and the resolution is 0.01%.

[0053] In the embodiment of the present invention, based on the actual flue gas data analysis of a thermal power station, the CO2 concentration in the flue gas is about 15%. Therefore, the setting of the CO2 concentration condition in this embodiment selects four different CO2 concentrations centered at 15%. The four CO2 concentrations corresponding to the correction coefficient regression model are 16.32%, 15.57%, 12.21%, and 12.48%, respectively. The four CO2 concentrations corresponding to the correction coefficient linear model are 19.26%, 17.97%, 15.78%, and 13.07%, respectively.

[0054] In the embodiment of the present invention, a gas pressure reducing valve is used to control the ratio of CO2 and N2, and the CO2 monitoring values ​​of the electrochemical flue gas analyzer at different temperatures and humidities are recorded when the CO2 concentration is 16.32%, as shown in Table 1 below:

[0055] Table 1 Statistics of CO2 monitoring values ​​of electrochemical flue gas analyzer when the concentration is 16.32%

[0056]

[0057] A gas pressure reducing valve was used to control the ratio of CO2 to N2, and the CO2 monitoring values ​​of the electrochemical flue gas analyzer at different temperatures and humidities were recorded when the CO2 concentration was 15.57%, as shown in Table 2 below:

[0058] Table 2 Statistics of CO2 monitoring values ​​of electrochemical flue gas analyzer when the concentration is 15.57%

[0059]

[0060] A gas pressure reducing valve was used to control the ratio of CO2 to N2, and the CO2 monitoring values ​​of the electrochemical flue gas analyzer at different temperatures and humidities were recorded when the CO2 concentration was 12.21%, as shown in Table 3 below:

[0061] Table 3 Statistics of CO2 monitoring values ​​of electrochemical flue gas analyzer when the concentration is 12.21%

[0062]

[0063]

[0064] A gas pressure reducing valve was used to control the ratio of CO2 to N2, and the CO2 monitoring values ​​of the electrochemical flue gas analyzer at different temperatures and humidities were recorded when the CO2 concentration was 12.48%, as shown in Table 4 below:

[0065] Table 4 Statistics of CO2 monitoring values ​​of electrochemical flue gas analyzer when the concentration is 12.48%

[0066]

[0067] The CO2 monitoring values ​​of the four types of flue gas measured by the electrochemical flue gas analyzer under different temperature and humidity conditions are plotted as a scatter plot, as shown in the figure below. Figure 3 As shown, through research comparison Figure 3The CO2 monitoring values ​​of the electrochemical flue gas analyzer under different temperature and humidity conditions for simulated flue gases of different concentrations are roughly the same. For simulated flue gases of different concentrations, the CO2 monitoring values ​​monitored by the electrochemical flue gas analyzer under different temperature and humidity conditions have a turning trend. For simulated flue gases of different concentrations at the same humidity, when the temperature is higher than 60°C, the changing trend of the CO2 monitoring values ​​of the flue gas analyzer turns; for simulated flue gases of different concentrations at the same temperature, when the humidity is higher than 50%, the changing trend of the CO2 monitoring values ​​of the flue gas analyzer turns. In this embodiment, the temperature and humidity range is divided into four intervals based on the changing trend of the CO2 monitoring values ​​of the flue gas analyzer, and the correction coefficient regression model of the electrochemical flue gas analyzer under different temperature and humidity is established in segments.

[0068] In the embodiment of the present invention, when the CO2 concentration in the simulated flue gas is 16.32%, the correction coefficients of the electrochemical flue gas analyzer under different temperature and humidity environments are shown in Table 5 below:

[0069] Table 5 Statistics of correction coefficients of electrochemical flue gas analyzer when the concentration is 16.32%

[0070]

[0071] When the CO2 concentration in the simulated flue gas is 15.57%, the correction coefficients of the electrochemical flue gas analyzer under different temperature and humidity environments are shown in Table 6 below:

[0072] Table 6 Statistical table of correction coefficients of electrochemical flue gas analyzer when the concentration is 15.57%

[0073]

[0074] When the CO2 concentration in the simulated flue gas is 12.21%, the correction coefficients of the electrochemical flue gas analyzer under different temperature and humidity environments are shown in Table 7 below:

[0075] Table 7 Statistical table of correction coefficients of electrochemical flue gas analyzer when the concentration is 12.21%

[0076]

[0077]

[0078] When the CO2 concentration in the simulated flue gas is 12.48%, the correction coefficients of the electrochemical flue gas analyzer under different temperature and humidity environments are shown in Table 8 below:

[0079] Table 8 Statistical table of correction coefficients of electrochemical flue gas analyzer when the concentration is 12.48%

[0080]

[0081] In order to further study the influence of temperature and humidity on CO2 concentration monitoring of electrochemical flue gas analyzer, 3D mapping surface drawing was performed on the correction coefficients of electrochemical flue gas analyzers of four simulated flue gases at different temperatures and humidity using Origin software, as shown in the figure. Figure 4 As shown in the figure, the trend of the correction coefficient of electrochemical flue gas analyzer under different temperature and humidity conditions is further studied. Figure 4 It can be seen that for simulated flue gas of different concentrations, the correction coefficients of the electrochemical flue gas analyzer under different temperature and humidity conditions are roughly the same. The CO2 measurement signals of the electrochemical analyzer of the four simulated flue gases have roughly the same change trends under the conditions of the same temperature and humidity. It can be seen that when the electrochemical flue gas analyzer measures the same type of flue gas, the CO2 monitoring value has the following change rules: when T≤60℃, At the same humidity, the CO2 monitoring value of the electrochemical flue gas analyzer increases with the increase of temperature. At the same temperature, the CO2 monitoring value of the electrochemical flue gas analyzer decreases with the increase of humidity. When T>60℃, At the same humidity, the CO2 monitoring value of the electrochemical flue gas analyzer decreases with the increase of temperature. At the same temperature, the CO2 monitoring value of the electrochemical flue gas analyzer increases with the increase of humidity. When T>60℃, At the same humidity, the CO2 monitoring value of the electrochemical flue gas analyzer decreases with the increase of temperature. At the same temperature, the CO2 monitoring value of the electrochemical flue gas analyzer decreases with the increase of humidity. When T≤60℃, At the same humidity, the CO2 monitoring value of the electrochemical flue gas analyzer increases with increasing temperature. At the same temperature, the CO2 monitoring value of the electrochemical flue gas analyzer increases with increasing humidity.

[0082] In the embodiment of the present invention, in step S3, the regression model of each correction coefficient is obtained by the following calculation formula:

[0083]

[0084]

[0085] Where y1 represents the correction coefficient regression model when the CO2 concentration is 16.32%; a1 represents the first regression coefficient; represents temperature data; b1 represents the second regression coefficient; T represents humidity data; c1 represents the third regression coefficient; y2 represents the correction coefficient regression model when the CO2 concentration is 15.57%; d1 represents the fourth regression coefficient; e1 represents the fifth regression coefficient; f1 represents the sixth regression coefficient; y3 represents the correction coefficient regression model when the CO2 concentration is 12.21%; g1 represents the seventh regression coefficient; h1 represents the eighth regression coefficient; i1 represents the ninth regression coefficient; y4 represents the correction coefficient regression model when the CO2 concentration is 12.48%; j1 represents the tenth regression coefficient; k1 represents the eleventh regression coefficient; l1 represents the twelfth regression coefficient.

[0086] In the embodiment of the present invention, a multiple regression equation is established for simulated flue gas of different concentrations under different temperature and humidity conditions by means of the multiple regression analysis method in the Origin software, so as to calculate the correction coefficient of the CO2 measurement signal of the electrochemical flue gas analyzer under different temperature and humidity environments. Assuming the correction coefficient is y, the correction coefficient regression model of the experimental simulated flue gas of different concentrations for temperature and humidity can be obtained:

[0087] ①When CO2 concentration is 16.32%

[0088]

[0089] ②When the CO2 concentration is 15.57%

[0090]

[0091] ③When the CO2 concentration is 12.21%

[0092]

[0093] ④When CO2 concentration is 12.48%

[0094]

[0095] The correlation coefficient R of the above regression equation is 2 The values ​​are all greater than 0.90, indicating that the correlation of the equation is good. By analyzing the trend diagram of the CO2 monitoring signal correction coefficient of the electrochemical flue gas analyzer at different temperatures and humidities, it is found that the CO2 correction coefficients measured by the flue gas analyzer are basically the same under the same environmental conditions for simulated flue gases of different concentrations, and the slope and intercept of the binary regression equation of the electrochemical flue gas analyzer under the same conditions are very close. Therefore, the coefficients and intercepts of the correction coefficient regression model of the simulated flue gases of four different concentrations under the same conditions are averaged to obtain the first correction coefficient. The calculation formula is as follows:

[0096]

[0097] Then in step S4, the CO2 monitoring accurate value is obtained by the following calculation formula:

[0098] C1=C i *Y1

[0099] Among them, C1 represents the CO2 monitoring accuracy value, C i represents the first CO2 monitoring value, and Y1 represents the first correction coefficient.

[0100] In the embodiment of the present invention, a gas pressure reducing valve is used to control the ratio of CO2 and N2, and the CO2 monitoring values ​​of the infrared flue gas analyzer at different temperatures and humidities are recorded when the CO2 concentration is 19.26%, as shown in Table 9 below:

[0101] Table 9 Statistics of CO2 monitoring values ​​of infrared flue gas analyzer when the concentration is 19.26%

[0102]

[0103] Use a gas pressure reducing valve to control the ratio of CO2 and N2, and record the CO2 monitoring values ​​of the infrared flue gas analyzer at different temperatures and humidities when the CO2 concentration is 17.97%, as shown in Table 10 below:

[0104] Table 10 Statistics of CO2 monitoring values ​​of infrared flue gas analyzer when the concentration is 17.97%

[0105]

[0106] Use a gas pressure reducing valve to control the ratio of CO2 and N2, and record the CO2 monitoring values ​​of the infrared flue gas analyzer at different temperatures and humidities when the CO2 concentration is 15.78%, as shown in Table 11 below:

[0107] Table 11 Statistics of CO2 monitoring values ​​of infrared flue gas analyzer when the concentration is 15.78%

[0108]

[0109] Use a gas pressure reducing valve to control the ratio of CO2 and N2, and record the CO2 monitoring values ​​of the infrared flue gas analyzer at different temperatures and humidities when the CO2 concentration is 13.07%, as shown in Table 12 below:

[0110] Table 12 Statistics of CO2 monitoring values ​​of infrared flue gas analyzer when the concentration is 13.07%

[0111]

[0112]

[0113] The CO2 concentration values ​​in four simulated flue gases measured by the infrared flue gas analyzer under different temperature and humidity conditions are plotted as a scatter plot, as shown in the figure below. Figure 5 As shown, through Figure 5 From the CO2 monitoring values ​​of the infrared flue gas analyzer under different concentrations, temperatures and humidities of simulated flue gas, it can be seen that with the increase of temperature and humidity, the change trends of the CO2 monitoring values ​​of the infrared flue gas analyzer under different concentrations of simulated flue gas are roughly the same. At the same humidity, the CO2 monitoring value of the infrared flue gas analyzer increases with the increase of temperature; at the same temperature, the CO2 monitoring value of the infrared flue gas analyzer decreases with the increase of humidity.

[0114] In the embodiment of the present invention, when the CO2 concentration in the simulated flue gas is 19.26%, the correction coefficients of the infrared flue gas analyzer under different temperature and humidity environments are shown in Table 13:

[0115] Table 13 Statistical table of correction coefficients of infrared flue gas analyzer when the concentration is 19.26%

[0116]

[0117] When the CO2 concentration in the simulated flue gas is 17.97%, the correction coefficients of the infrared flue gas analyzer under different temperature and humidity environments are shown in Table 14:

[0118] Table 14 Statistical table of correction coefficients of infrared flue gas analyzer when the concentration is 17.97%

[0119]

[0120]

[0121] When the CO2 concentration in the simulated flue gas is 15.78%, the correction coefficients of the infrared flue gas analyzer under different temperature and humidity environments are shown in Table 15:

[0122] Table 15 Statistical table of correction coefficients of infrared flue gas analyzer when the concentration is 15.78%

[0123]

[0124] When the CO2 concentration in the simulated flue gas is 13.07%, the correction coefficients of the infrared flue gas analyzer under different temperature and humidity environments are shown in Table 16:

[0125] Table 16 Statistical table of correction coefficients of infrared flue gas analyzer when the concentration is 13.07%

[0126]

[0127] Origin software is used to perform 3D mapping of the correction coefficients of four infrared flue gas analyzers with different temperatures and humidity for simulated flue gases, such as Figure 6 As shown in the figure, the trend of the correction coefficient of infrared flue gas analyzer under different temperature and humidity conditions is further studied. Figure 6 From the trend diagram of the correction coefficient changes of the infrared flue gas analyzers with four simulated flue gas concentrations at different temperatures and humidities, it can be seen that the change trends of the CO2 measurement signals of the infrared flue gas analyzers with four simulated flue gases are roughly the same under the conditions of the same temperature and humidity.

[0128] In the embodiment of the present invention, in step S3, the linear model of each correction coefficient is obtained by the following calculation formula:

[0129]

[0130] Wherein, y5 represents the correction coefficient linear model when the CO2 concentration is 19.26%; a2 represents the first linear coefficient; Represents temperature data; b2 represents the second linear coefficient; T represents humidity data; c2 represents the third linear coefficient; y6 represents the correction coefficient linear model when the CO2 concentration is 17.97%; d2 represents the fourth linear coefficient; e2 represents the fifth linear coefficient; f2 represents the sixth linear coefficient; y7 represents the correction coefficient linear model when the CO2 concentration is 15.78%; g2 represents the seventh linear coefficient; h2 represents the eighth linear coefficient; i2 represents the ninth linear coefficient; y8 represents the correction coefficient linear model when the CO2 concentration is 13.07%; j2 represents the tenth linear coefficient; k2 represents the eleventh linear coefficient; l2 represents the twelfth linear coefficient.

[0131] In the embodiment of the present invention, a correction coefficient linear model of simulated flue gas at different temperatures and humidities is established using multiple regression. The second CO2 monitoring value of the simulated flue gas at different temperatures and humidities is corrected using this correction coefficient linear model. The calculation formula is as follows:

[0132] ①When the CO2 concentration is 19.26%

[0133]

[0134] ②When the CO2 concentration is 17.97%

[0135]

[0136] ③When the CO2 concentration is 15.78%

[0137]

[0138] ④When CO2 concentration is 13.07%

[0139]

[0140] The correlation coefficient R of the above linear equation 2 They are all greater than 0.91, indicating that the correlation of the equation is good. By analyzing the correction coefficients of the CO2 monitoring signal of the infrared flue gas analyzer at different temperatures and humidities, it is found that the correction coefficients measured by the flue gas analyzer are basically the same under the same environmental conditions for simulated flue gases of different concentrations, and the slope and intercept of the binary regression equation under the same conditions are very close. Therefore, the coefficients and intercepts of the correction coefficient linear model of the four different concentrations of simulated flue gases under the same conditions are averaged to obtain the second correction coefficient. The calculation formula is as follows:

[0141]

[0142] Then in step S4, the CO2 monitoring accurate value is obtained by the following calculation formula:

[0143] C2=C i *Y2

[0144] Among them, C2 represents the CO2 monitoring accuracy value, C i represents the second CO2 monitoring value, and Y1 represents the second correction coefficient.

[0145] In the description of the present invention, the reference terms "one embodiment", "some embodiments", "in the present embodiment", "specific examples", or "some examples" mean that the specific features, mechanisms, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, mechanisms, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0146] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for monitoring CO2 from stationary emission sources based on an adaptive compensation model, characterized in that: A gas source module, a temperature and humidity control module, and a flue gas monitoring module are pre-built and connected in sequence. The gas source module is used to collect N2 gas and CO2 gas to obtain a mixed gas. The temperature and humidity control module is used to heat or humidify the mixed gas. The flue gas monitoring module includes a thermometer, a hygrometer, an electrochemical flue gas analyzer or an infrared flue gas analyzer. The fixed emission source CO2 monitoring method includes the following steps: Step S1, controlling the gas source module to adjust and obtain the mixed gas with various CO2 concentrations; Step S2, controlling the thermometer to collect temperature data of each of the mixed gases, and controlling the hygrometer to collect humidity data of each of the mixed gases; Step S3, controlling the electrochemical flue gas analyzer to establish a correction coefficient regression model corresponding to each CO2 concentration based on the temperature data and the humidity data, and performing a averaging process on each correction coefficient regression model to obtain a first correction coefficient; or Controlling the infrared flue gas analyzer to establish a correction coefficient linear model corresponding to each CO2 concentration based on the temperature data and the humidity data, and performing a homogenization process on each correction coefficient linear model to obtain a second correction coefficient; Step S4, obtaining the first CO2 monitoring value or the second CO2 monitoring value obtained by the electrochemical flue gas analyzer at the current moment, and combining the first correction coefficient or the second correction coefficient to obtain the corresponding CO2 monitoring accurate value.

2. The method for monitoring CO2 from a stationary emission source according to claim 1, wherein: The gas source module includes a first gas pressure reducing valve and a second gas pressure reducing valve, and the first gas pressure reducing valve and the second gas pressure reducing valve are respectively connected to the temperature and humidity control module through gas pipes. The first gas pressure reducing valve is used to collect the N2 gas and adjust the concentration ratio of the N2 gas in the mixed gas, and the second gas pressure reducing valve is used to collect the CO2 gas and adjust the concentration ratio of the CO2 gas in the mixed gas.

3. The method for monitoring CO2 from a stationary emission source according to claim 1, wherein: The flue gas monitoring module includes a flue gas duct, the thermometer, the hygrometer and a flue gas analyzer. The flue gas analyzer is the electrochemical flue gas analyzer or the infrared flue gas analyzer. One end of the flue gas duct is connected to the temperature and humidity control module, and the other end of the flue gas duct is connected to the outdoors. The thermometer and the hygrometer are arranged on the inner wall of the flue gas duct, and the flue gas analyzer is arranged outside the flue gas duct and is connected to the inside of the flue gas duct.

4. The method for monitoring CO2 from a stationary emission source according to claim 1, wherein: The CO2 concentrations are 16.32%, 15.57%, 12.21%, 12.48%, 19.26%, 17.97%, 15.78%, and 13.07%, respectively. Then, in step S3, the correction coefficient regression model corresponding to the CO2 concentrations of 16.32%, 15.57%, 12.21%, and 12.48% is established; or The correction coefficient linear model corresponding to the CO2 concentration of 19.26%, 17.97%, 15.78% and 13.07% was established.

5. The method for monitoring CO2 from a stationary emission source according to claim 4, wherein: In step S3, the correction coefficient regression model is obtained by the following calculation formula: Wherein, y1 represents the correction coefficient regression model when the CO2 concentration is 16.32%; a1 represents the first regression coefficient; represents the temperature data; b1 represents the second regression coefficient; T represents the humidity data; c1 represents the third regression coefficient; y2 represents the correction coefficient regression model when the CO2 concentration is 15.57%; d1 represents the fourth regression coefficient; e1 represents the fifth regression coefficient; f1 represents the sixth regression coefficient; y3 represents the correction coefficient regression model when the CO2 concentration is 12.21%; g1 represents the seventh regression coefficient; h1 represents the eighth regression coefficient; i1 represents the ninth regression coefficient; y4 represents the correction coefficient regression model when the CO2 concentration is 12.48%; j1 represents the tenth regression coefficient; k1 represents the eleventh regression coefficient; l1 represents the twelfth regression coefficient.

6. The method for monitoring CO2 from a stationary emission source according to claim 4, wherein: In step S3, the linear model of each correction coefficient is obtained by the following calculation formula: Wherein, y5 represents the correction coefficient linear model when the CO2 concentration is 19.26%; a2 represents the first linear coefficient; Represents the temperature data; b2 represents the second linear coefficient; T represents the humidity data; c2 represents the third linear coefficient; y6 represents the correction coefficient linear model when the CO2 concentration is 17.97%; d2 represents the fourth linear coefficient; e2 represents the fifth linear coefficient; f2 represents the sixth linear coefficient; y7 represents the correction coefficient linear model when the CO2 concentration is 15.78%; g2 represents the seventh linear coefficient; h2 represents the eighth linear coefficient; i2 represents the ninth linear coefficient; y8 represents the correction coefficient linear model when the CO2 concentration is 13.07%; j2 represents the tenth linear coefficient; k2 represents the eleventh linear coefficient; l2 represents the twelfth linear coefficient.

7. The method for monitoring CO2 from a stationary emission source according to claim 5, wherein: In step S3, the value range of the first regression coefficient is 0.00335–0.00337, the value range of the second regression coefficient is 0.00302–0.00304, the value range of the third regression coefficient is 0.99266–0.99463, the value range of the fourth regression coefficient is 0.00262–0.00264, the value range of the fifth regression coefficient is 0.00521–0.00524, and the value range of the sixth regression coefficient is 0.50130–0.502 52, the value range of the seventh regression coefficient is 0.00220–0.00222, the value range of the eighth regression coefficient is 0.00266–0.00269, the value range of the ninth regression coefficient is 1.25804–1.25986, the value range of the tenth regression coefficient is 0.00254–0.00257, the value range of the eleventh regression coefficient is 0.00509–0.00512, and the value range of the twelfth regression coefficient is 0.77078–0.77328.

8. The method for monitoring CO2 from a stationary emission source according to claim 7, wherein: In step S3, the first correction coefficient is obtained by the following calculation formula: Wherein, Y1 represents the first correction coefficient, represents the temperature data, and T represents the humidity data.

9. The method for monitoring CO2 from a stationary emission source according to claim 6, wherein: In step S3, the value of the first linear coefficient is 0.00135, the value of the second linear coefficient is 0.00202, the value of the third linear coefficient is 1.02017, the value of the fourth linear coefficient is 0.00133, the value of the fifth linear coefficient is 0.00204, the value of the sixth linear coefficient is 1.01996, the value of the seventh linear coefficient is 0.00133, the value of the eighth linear coefficient is 0.00203, the value of the ninth linear coefficient is 1.02136, the value of the tenth linear coefficient is 0.00134, the value of the eleventh linear coefficient is 0.00203, and the value of the twelfth linear coefficient is 1.02235.

10. The method for monitoring CO2 from a stationary emission source according to claim 9, wherein: In step S3, the second correction coefficient is obtained by the following calculation formula: Wherein, Y2 represents the second correction coefficient, represents the temperature data, and T represents the humidity data.

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