Carbon dioxide flue gas emission monitoring calibration method and related devices

Abstract

This invention discloses a method and related apparatus for monitoring and calibrating carbon dioxide flue gas emissions. The method includes: dividing the actual emission characteristics of a target industry factory into multiple simulated emission source scenarios, where the carbon dioxide gas generation parameters, particulate matter aerosol generation parameters, and environmental parameters differ across the simulated emission source scenarios; controlling the carbon dioxide flue gas emission monitoring standard measuring device according to the different simulated emission source scenarios to provide corresponding test parameters; and controlling the test sequence of the multiple simulated emission source scenarios in conjunction with the actual emission patterns of the target industry factory to calibrate the carbon dioxide emission measuring instrument. This method addresses the problem that as carbon emission requirements tighten, these industries face more stringent emission control requirements, necessitating continuous optimization of detection technology and improvement of data accuracy.

Description

Technical Field

[0001] This application relates to the field of carbon metering, and more specifically, to a method and related apparatus for monitoring and calibrating carbon dioxide flue gas emissions. Background Technology

[0002] With the goal of actively exploring a green and low-carbon development path, the carbon trading market is receiving increasing attention as an important means of emission control. Accurate, objective, real-time, and credible carbon emission data are a crucial foundation for the efficient operation of the carbon trading market.

[0003] Carbon dioxide emission monitoring is crucial in high-energy-consuming and high-emission industries such as thermal power, building materials, and steel. As carbon emission requirements tighten, these industries face more stringent emission control demands, necessitating continuous optimization of monitoring technologies and improvement of data accuracy. Summary of the Invention

[0004] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. The summary section of this invention is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] To address the challenges posed by increasingly stringent emission control requirements in these industries, necessitating continuous optimization of detection technologies and improved data accuracy, this invention proposes a carbon dioxide flue gas emission monitoring calibration method for a carbon dioxide flue gas emission monitoring standard measurement device. This device is used to calibrate carbon dioxide concentration measuring instruments. The device is constructed in a straight wind tunnel configuration to generate carbon dioxide gas and particulate matter aerosols. The device also includes a measurement traceability module and a transparent test window at the measurement location. The method comprises:

[0006] Based on the actual emission characteristics of the target industry factories, multiple simulation scenarios of actual emission sources are divided. The carbon dioxide gas generation parameters, particulate matter aerosol generation parameters, and environmental parameters are different in different simulation scenarios of emission sources.

[0007] The carbon dioxide flue gas emission monitoring standard measuring device is controlled according to different actual emission source simulation scenarios to provide corresponding test parameters;

[0008] By combining the actual emission patterns of target industry factories, the test sequence of multiple actual emission source simulation scenarios is controlled in order to calibrate the carbon dioxide emission measurement instrument.

[0009] Optional, also includes:

[0010] The pollution risk level of multiple actual emission source simulation scenarios is evaluated based on the test parameters of different emission source simulation scenarios, so as to divide the multiple actual emission source simulation scenarios into test window pollution scenarios and test window clean scenarios based on the test window pollution risk level.

[0011] If the previous actual emission source simulation scenario is a test window pollution scenario, the carbon dioxide flue gas emission monitoring standard measurement device is controlled to provide corresponding test parameters to provide a test window clean scenario, and then switched to the next actual emission source simulation scenario in the test sequence after a predetermined time.

[0012] Optionally, when the previous actual emission source simulation scenario is a test window pollution scenario, controlling the carbon dioxide flue gas emission monitoring standard measuring device to provide corresponding test parameters to provide a test window clean scenario, and then switching to the next actual emission source simulation scenario in the test sequence after a predetermined time, includes:

[0013] If the previous actual emission source simulation scenario is an electrified mine dust emission scenario, the carbon dioxide flue gas emission monitoring standard measurement device is controlled to provide corresponding test parameters to provide desulfurization-related emission scenarios, and then switched to the next actual emission source simulation scenario in the test sequence after a predetermined time.

[0014] Optional, also includes:

[0015] The pollution risk level of multiple actual emission source simulation scenarios is evaluated based on the test parameters of different emission source simulation scenarios, so as to divide the multiple actual emission source simulation scenarios into test window pollution scenarios and prevention scenarios associated with these scenarios based on the test window pollution risk level.

[0016] Before providing the test window pollution scenario according to the test sequence, the carbon dioxide flue gas emission monitoring standard measuring device is controlled to provide corresponding test parameters to provide the prevention scenario, and then switched to the test window pollution scenario after a predetermined time.

[0017] Optionally, the step of controlling the carbon dioxide flue gas emission monitoring standard measuring device to provide corresponding test parameters to provide the prevention scenario before providing the test window pollution scenario according to the test sequence, and then switching to the test window pollution scenario after a predetermined period of time, includes:

[0018] Before providing the charged mine dust emission scenario according to the test sequence, the carbon dioxide flue gas emission monitoring standard measuring device is controlled to provide corresponding test parameters to provide desulfurization-related emission scenarios, and then switched to the charged mine dust emission scenario after a predetermined time.

[0019] Optional, also includes:

[0020] High-precision image data of the same transparent window segment of the transparent test window are periodically acquired;

[0021] The high-precision image data acquired in two consecutive periods are analyzed. The frame difference method is used to compare the differences between the two image data to identify the contamination points that coexist in the two periods.

[0022] The contours of the contaminated areas are extracted by combining threshold segmentation with morphological operations;

[0023] Calculate the geometric features of the contaminated area, and based on the location of the contaminated area, calculate the laser path that may be affected by the laser tracing module.

[0024] In the event of an affected laser path, the calibration analysis of the instrument to be calibrated by the metrology traceability module is suspended. The metrology traceability module includes the LDV and TDLAS gas analyzers.

[0025] Optional, also includes

[0026] Determine a new measurement angle to ensure the laser avoids contaminated areas;

[0027] The incident angle of the measurement traceability module is shifted to avoid contamination points.

[0028] Secondly, this invention also proposes a carbon dioxide flue gas emission monitoring standard measurement device for calibrating carbon dioxide emission measuring instruments. The carbon dioxide flue gas emission monitoring standard measurement device is constructed in a straight wind tunnel configuration to generate carbon dioxide gas and particulate matter aerosols. The device also includes a value traceability module and a transparent test window at the measurement location. The carbon dioxide flue gas emission monitoring standard measurement device comprises:

[0029] The division unit is used to divide multiple actual emission source simulation scenarios based on the actual emission characteristics of the target industry factory. The carbon dioxide gas generation parameters, particulate matter aerosol generation parameters, and environmental parameters are different in different emission source simulation scenarios.

[0030] The control unit is used to control the carbon dioxide flue gas emission monitoring standard measuring device according to different actual emission source simulation scenarios to provide corresponding test parameters;

[0031] The testing unit is used to control the testing sequence of multiple simulated emission source scenarios based on the actual emission patterns of factories in the target industry, in order to calibrate the carbon dioxide emission measuring instrument.

[0032] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the carbon dioxide flue gas emission monitoring and calibration method as described in any of the first aspects above.

[0033] Fourthly, the present invention also proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the carbon dioxide flue gas emission monitoring and calibration method of any of the above claims in the first aspect.

[0034] In summary, the carbon dioxide flue gas emission monitoring and calibration method proposed in this application divides multiple simulated emission source scenarios based on the actual emission characteristics of the target industry factory. The carbon dioxide gas generation parameters, particulate matter aerosol generation parameters, and environmental parameters differ in each simulated emission source scenario. The method controls the standard measuring device for carbon dioxide flue gas emission monitoring according to the different simulated emission source scenarios to provide corresponding test parameters. Furthermore, it controls the test sequence of multiple simulated emission source scenarios in conjunction with the actual emission patterns of the target industry factory to calibrate the carbon dioxide emission measuring instrument. By simulating the emission characteristics of different factories and controlling parameters such as carbon dioxide concentration, particulate matter concentration, flow rate, temperature, and humidity according to real operating conditions, the measurement accuracy and stability of the carbon dioxide concentration measuring instrument can be more comprehensively evaluated and calibrated. This approach ensures the reliability of the measuring instrument under various industrial emission environments, thus overcoming the limitations of traditional calibration methods that are confined to a single operating condition. By constructing multiple simulated emission source scenarios, dynamically controlling the parameters of the standard measuring device, and adjusting the test sequence according to the factory's emission patterns, the adaptability and calibration accuracy of the carbon dioxide measuring instrument are comprehensively improved. Compared to traditional calibration methods under a single environment, this method can more realistically reflect industrial emission conditions, enabling measuring instruments to maintain high accuracy and stability in various complex environments, and providing a reliable standard measurement system for environmental monitoring, industrial emission control and laboratory testing.

[0035] The carbon dioxide flue gas emission monitoring and calibration method of the present invention, and other advantages, objectives and features of the present invention will be apparent in part from the following description, and in part from the understanding of those skilled in the art through study and practice of the present invention. Attached Figure Description

[0036] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0037] Figure 1This is a schematic flowchart of a carbon dioxide flue gas emission monitoring and calibration method provided in an embodiment of this application;

[0038] Figure 2 This is a schematic diagram of a carbon dioxide flue gas emission monitoring and calibration device provided in an embodiment of this application;

[0039] Figure 3 This is a schematic diagram of the structure of an electronic device for monitoring carbon dioxide flue gas emissions, provided in an embodiment of this application. Detailed Implementation

[0040] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0041] To address the challenges these industries face with increasingly stringent carbon emission control requirements, necessitating continuous optimization of detection technologies and improved data accuracy, please refer to [link to relevant documentation]. Figure 1 This is a schematic flowchart of a carbon dioxide flue gas emission monitoring calibration method provided in an embodiment of this application. It is used for a carbon dioxide flue gas emission monitoring standard measurement device, which is used to calibrate carbon dioxide concentration measuring instruments. The carbon dioxide flue gas emission monitoring standard measurement device is constructed in a straight wind tunnel configuration, specifically for generating carbon dioxide gas and particulate matter aerosols. The carbon dioxide flue gas emission monitoring standard measurement device also includes a value traceability module and has a transparent test window at the measurement location. The specific method may include steps S110 to S130.

[0042] S110 divides multiple actual emission source simulation scenarios based on the actual emission characteristics of target industry factories. The carbon dioxide gas generation parameters, particulate matter aerosol generation parameters, and environmental parameters are different in different emission source simulation scenarios.

[0043] S120 controls the carbon dioxide flue gas emission monitoring standard measuring device according to different actual emission source simulation scenarios to provide corresponding test parameters.

[0044] S130 combines the actual emission patterns of target industry factories to control the test sequence of multiple actual emission source simulation scenarios in order to calibrate carbon dioxide emission measurement instruments.

[0045] For example, based on the ultra-low emission characteristics of industries such as thermal power, building materials, and steel, a standard measurement device for velocity / concentration can be established to simulate the multiphase flow field of greenhouse gas emissions from stationary sources. Fly ash or dispersed polyalphaolefins are selected as ultrafine particles. The generation rate of the air generator and the carbon dioxide gas velocity are quantitatively controlled. A Venturi device is used to continuously and stably generate polyalphaolefin or fly ash aerosols, and the airflow pressure or flow rate is adjusted to control the concentration and range of aerosol generation. To ensure the stability of the flow field, the device is constructed according to the relevant standards for straight wind tunnels, with pre-reserved fiber optic mounting holes and transparent test windows at the measurement locations to meet the installation requirements of measuring instruments based on different principles. For traceability, L-shaped Pitot tubes, temperature and humidity sensors, and carbon dioxide gas analyzers are used as the main standard devices to achieve real-time measurement of flow field velocity / carbon dioxide concentration. The device can also be compared with instruments such as laser Doppler velocimeters and TDLAS gas analyzers. To improve the measurement stability of the device, a PID integrated control system is developed to monitor the device's flow velocity and dust concentration in real time, and adjust process instruments such as centrifugal fans, compressors, and flow controllers to achieve automated control of the device. The development of this device provides a new calibration scheme for instruments monitoring carbon dioxide emissions from stationary emission sources, changing the existing situation where flow rate and carbon dioxide concentration are traced separately, and providing a solution for verifying and evaluating the overall measurement performance and traceability of the instrument.

[0046] Understandably, to accurately simulate the characteristics of emission sources in different industrial sectors, it is necessary to divide the simulation scenarios into multiple actual emission source scenarios based on the specific emission conditions of the target industry's factories, so that the operating conditions of the standard measuring device can cover different types of carbon dioxide emission sources. For example, in thermal power plants, the carbon dioxide concentration emitted by coal-fired boilers is generally between 10% and 14%, accompanied by 10–50 mg / m³. 3 Fly ash particles are emitted at temperatures between 80 and 120°C, with typically low humidity. In contrast, during clinker calcination in cement plants, CO2 concentrations are higher, typically 14%–18%, with particulate matter mainly consisting of alkaline calcium oxide dust. Emission temperatures reach as high as 100–180°C, with lower humidity. In the exhaust gas from sintering machines in the steel industry, CO2 concentrations are generally 8%–12%, with particulate matter primarily consisting of metal oxides (Fe2O3), reaching concentrations of 20–50 mg / m³. 3Temperatures in typical industrial environments range from 80 to 160°C, with moderate humidity. In contrast, coke oven flue gas from coking plants typically exhibits CO2 concentrations of 5% to 10%, accompanied by some tar particles and higher humidity (above 40%). This classification ensures that standard measuring devices can provide accurate simulated emission conditions in different industrial scenarios, thereby verifying the adaptability and measurement stability of measuring instruments under various real-world operating conditions.

[0047] For example, after establishing the simulation scenario, it is necessary to further determine the generation methods of carbon dioxide gas and particulate matter to ensure that parameters such as concentration, particle size, and flow rate are consistent with actual operating conditions. Carbon dioxide gas generation uses a high-purity CO2 gas source and is precisely controlled using a mass flow controller (MFC) to allow the CO2 concentration to be adjusted under different operating conditions. For example, in simulating a coal-fired power plant, the CO2 concentration is set at 10–14%, while in a cement kiln simulation scenario, it is set at 14–18%. Particulate matter generation typically employs a Venturi jet device or a pneumatic conveying system to ensure the suspension stability of particles in the airflow, and the particle concentration is controlled by adjusting the gas supply pressure and dust supply rate. For example, in simulating the tail gas from steelmaking, high-temperature Fe2O3 particles can be used, with their concentration set at 20–50 mg / m³. 3 To further match industrial operating conditions, the system also needs to be equipped with temperature and humidity control modules. For example, when simulating flue gas from a thermal power plant after wet desulfurization, the humidity needs to be increased to above 40%, and the temperature needs to be set to 60–80℃ to verify the stability of the instrument in high humidity and low temperature environments.

[0048] For example, after determining the emission parameters for different industrial scenarios, the control system of a standard measurement device for carbon dioxide flue gas emission monitoring needs to dynamically adjust the relevant parameters to provide an accurate testing environment. This standard measurement device adopts a straight wind tunnel structure to ensure uniform flow of gas and particulate matter in the pipeline and reduce the impact of turbulence on the measurement. During the experiment, the system first adjusts the CO2 concentration according to the set emission source simulation scenario, using a high-precision MFC to control the CO2 mass flow rate, such as setting 12% CO2 in the simulation of a thermal power plant and 16% in the cement kiln condition. Subsequently, the particulate matter generation system is activated, and the aerosol concentration is controlled by adjusting the injection pressure and particle supply rate, such as setting the tar particle concentration to 10–40 mg / m³ in the simulation of a coking plant. 3After ensuring accurate gas composition, the system needs to adjust the wind speed and flow pattern to match the exhaust characteristics of different emission sources. For example, in high-temperature industrial emissions (such as steel combustion exhaust gas), the airflow velocity may be high, so the wind tunnel needs to adjust the wind speed to 8–10 m / s, while in low-velocity emission sources (such as cement kiln exhaust gas), it needs to be reduced to 3–5 m / s. Wind speed is measured using an L-shaped Pitot tube and a laser Doppler velocimeter (LDV), and the wind speed is adjusted by PID control of the fan. In addition, to meet the measurement needs of different operating conditions, the device is also equipped with a temperature and humidity control system. For example, when simulating flue gas after wet desulfurization, the humidity is increased to over 50% by a humidifier to observe the drift of the measuring instruments in a high-humidity environment.

[0049] Understandably, since industrial emissions are not constant but dynamically change with different stages of the production process, the testing sequence for different scenarios should be controlled according to actual emission patterns during calibration to more closely approximate real-world conditions. For example, in the experimental process of a thermal power plant, the initial combustion emission stage (high-temperature fly ash, high CO2 concentration) should be simulated first, followed by the post-desulfurization emission stage (high humidity, low-temperature CO2), and finally the post-dust removal purification emission stage (low particulate CO2 concentration). In the testing process of a cement plant, the kiln head emission (high temperature, high CO2) should be simulated first, followed by the particulate matter purification stage, and finally the low-temperature, low-dust emission stage. This multi-stage testing method makes the calibration process more consistent with real emission conditions, thereby ensuring that the measuring instrument can operate stably throughout the entire emission cycle. Furthermore, during the testing process, it is necessary to continuously compare the measurement results of the reference device (TDLAS) and the instrument under test to analyze the errors in the measurement data under different scenarios. For example, in a wet desulfurization environment, if the CO2 reading of the measuring instrument is too low, it may be due to absorption errors caused by water vapor interference; while in a high-dust environment, if the measured value is too high, it may be due to errors caused by particulate matter scattering effects. Through dynamic testing in multiple scenarios, the sources of instrument error can be systematically analyzed, and its measurement accuracy can be optimized accordingly.

[0050] In summary, the carbon dioxide flue gas emission monitoring and calibration method provided in this application divides multiple simulated emission source scenarios based on the actual emission characteristics of the target industry factory. The carbon dioxide gas generation parameters, particulate matter aerosol generation parameters, and environmental parameters differ in each simulated emission source scenario. The method controls the carbon dioxide flue gas emission monitoring standard measuring device according to the different simulated emission source scenarios to provide corresponding test parameters. Furthermore, it controls the test sequence of the multiple simulated emission source scenarios in conjunction with the actual emission patterns of the target industry factory to calibrate the carbon dioxide emission measuring instrument. By simulating the emission characteristics of different factories and controlling parameters such as carbon dioxide concentration, particulate matter concentration, flow rate, temperature, and humidity according to real operating conditions, the measurement accuracy and stability of the carbon dioxide concentration measuring instrument can be more comprehensively evaluated and calibrated. This approach ensures the reliability of the measuring instrument under various industrial emission environments, thereby overcoming the limitations of traditional calibration methods that are restricted to a single operating condition.

[0051] According to some embodiments, it also includes:

[0052] The pollution risk level of multiple actual emission source simulation scenarios is evaluated based on the test parameters of different emission source simulation scenarios, so as to divide the multiple actual emission source simulation scenarios into test window pollution scenarios and test window clean scenarios based on the test window pollution risk level.

[0053] If the previous actual emission source simulation scenario is a test window pollution scenario, the carbon dioxide flue gas emission monitoring standard measurement device is controlled to provide corresponding test parameters to provide a test window clean scenario, and then switched to the next actual emission source simulation scenario in the test sequence after a predetermined time.

[0054] Understandably, current calibration devices primarily calibrate measurement equipment by simulating specific airflow and particulate matter distributions. However, actual emission sources typically exhibit more complex and dynamically changing characteristics. For instance, gases and particulate matter in real emission sources may experience instantaneous fluctuations, non-uniform distributions, and varying temperatures and humidity levels, while the device can only create a relatively stable simulated environment. Therefore, the aforementioned method simulates real emission scenarios and, based on the actual emission patterns of the factory, alternately combines these simulated scenarios in a specific order (e.g., ordered as scenario a-scenario b-scenario c-scenario d…) to simulate the actual emission patterns of the factory. Based on this, it can be understood that some emission scenarios may lead to contamination of the transparent viewing window due to differences in the composition, concentration, flow rate, and even humidity of the emissions. These scenarios can be defined as test window contamination scenarios (for example, analysis shows that in simulation scenario a, due to low humidity, low flow rate, and the presence of metallic components in the emissions, they easily adhere to the test window based on electrostatic attraction; therefore, scenario a can be defined as a contamination scenario). Conversely, different emission parameters in some emission scenarios may have a cleaning effect on the viewing window during simulated emissions; these scenarios can be defined as test window cleaning scenarios (for example, scenario d can be defined as a cleaning scenario). Therefore, in the simulated factory emissions... When switching between different simulated emission scenarios using a regular pattern, further consideration should be given to maintaining a clean viewing window. In the original scenario sorting process, if a certain simulated scenario is considered a contaminated test window scenario, a clean scenario can be placed after it in the sorting to ensure window cleanliness (e.g., the original sorting is scenario a-scenario b-scenario c-scenario d…). Based on the above principle, this can be changed to (scenario a-scenario d-scenario b-scenario c-scenario d…). It is important to emphasize that a so-called clean scenario does not refer to an ideal airflow environment with inherent cleaning properties, but rather to a specific contamination mechanism based on the preceding contaminated scenario. Through the relative offsetting effect of physical parameters, it achieves the purpose of local mitigation or interference suppression of contamination in the transparent test window. For example, scenario a (low flow rate, low humidity, high electrostatic dust) will cause electrostatically adsorbed particles to accumulate in the test window; scenario d (high humidity, particle anti-agglomeration, local microturbulence) can precisely desorb or re-entrain previously attached particles into the airflow; however, scenario d has no cleaning effect on scenario b (oil film contamination caused by high-viscosity aerosols). Therefore, scenario d is not a universally clean scenario, but a clean scenario relative to scenario a. This breaks through the limitations of traditional single steady-state testing environments. Relatively clean scenarios are not obvious because they lack universal cleanliness. They require judgment based on contamination mechanisms and reverse parameter analysis. With the help of intelligent optimization strategies for scenario order, the number of downtimes is reduced while avoiding contamination of the test window. No additional cleaning system is needed. The test window contamination is naturally and gradually released through scenario rearrangement, which simplifies engineering practice and has self-maintenance characteristics.This invention supports long-term continuous calibration of instruments, significantly reducing the frequency of manual cleaning and maintenance, and improving system efficiency and operational stability. It fully utilizes the influence of various parameters (such as flow rate, humidity, particle charge, and particle size distribution) on the contamination evolution of a transparent test window in simulated scenarios. By comparing the physical counterbalancing effects between contaminated scenarios and other scenarios, it dynamically identifies "relatively clean scenarios" with contamination cleaning potential. These clean scenarios do not inherently possess cleaning capabilities but exhibit a neutralizing or mitigating effect on contamination pathways in the face of specific contamination mechanisms. This is a parameter-logic-driven approach to discovering clean relationships, significantly different from traditional physical cleaning systems or absolute purging fields. The introduction of clean scenarios is not externally imposed but rather a rational utilization of certain stages in the factory's emission cycle (such as desulfurization and wet dust removal stages), combined with their parameter characteristics (such as high humidity, high flow rate, and anti-agglomeration effects) to intervene in the contamination trends of the previous scenario. This proactive approach saves system resources and avoids introducing additional interference signals, representing an embedded optimization strategy. By adjusting the test sequence and embedding specific pollution scenarios and corresponding relatively clean scenarios into the overall test cycle, the dynamic realism of the simulated emission patterns is maintained, while pollution accumulation in the test window is effectively controlled. This sequence optimization strategy improves the reliability of the calibration environment while minimizing human intervention and reliance on additional cleaning modules, offering significant advantages in engineering implementation simplicity and test continuity. Under complex emission conditions, the pollution-to-clean linkage sequence constructed through scenario parameter logic allows the measuring instrument to not only undergo performance verification in representative pollution environments but also maintain transparency to critical paths (such as the laser channel) throughout the continuous operating cycle, thereby improving the calibration accuracy and reliability of the evaluation results under actual operating conditions. Compared to traditional methods that directly add cleaning mechanisms (such as window air scrapers or automatic cleaning processes), this invention does not introduce physical cleaning devices but actively discovers the next scenario with a cleaning effect through in-depth analysis of the parameter coupling relationship between scenarios and integrates it into the emission pattern simulation sequence, demonstrating a highly innovative and professional logical chain.

[0055] For example, firstly, based on the actual emission behavior of the target industry (e.g., boiler section, desulfurization section, dust removal section, etc. in thermal power industry), multiple emission source simulation scenarios (such as a, b, c, d, etc.) are designed. Each scenario defines a specific combination of parameters, including airflow velocity, particulate matter concentration, particle size distribution, humidity, temperature, and CO2 concentration. The corresponding physical environment is realized through controllable gas and particulate matter generation units. By modeling and analyzing historical emission data of the target industry's factories, the typical emission sequence and switching patterns in its process cycle are determined. For example, a common sequence might be: Scenario a (electrostatic dust) → Scenario b (low-humidity condensation) → Scenario c (high-velocity flue gas) → Scenario d (wet desulfurization), forming periodic or non-periodic combination rules. In each scenario, the risk mechanism of window contamination is determined based on measured or simulated data, such as whether: excessively long particle residence time (low velocity); charged particle adsorption; adhesion of condensed wet dust film; or thermal melting adsorption of particles due to high temperature. Such scenarios are defined as window contamination scenarios. For each pollution scenario, its pollution source mechanism and physical characteristics are analyzed. It is then searched among other defined scenarios to identify those that can counteract the pollution form through desorption / purging / anti-agglomeration effects based on parameters such as airflow, particle kinetic energy, and humidity. These scenarios are considered relatively clean scenarios (e.g., d relative to a). If the original plan was a→b→c→d, then when a is identified as a pollution scenario and d has a relative cleaning effect on it, d is inserted to form: a→d→b→c→d. However, if b is also a pollution scenario and d has no cleaning effect on b, it is necessary to further search for another clean scenario e that only affects b, thus forming: a→d→b→e→c→d. This establishes a dynamic alternation strategy from pollution to cleanliness.

[0056] In some examples, the step of controlling the carbon dioxide flue gas emission monitoring standard measuring device to provide corresponding test parameters to provide a clean test window scenario when the previous actual emission source simulation scenario is a polluted test window scenario, and then switching to the next actual emission source simulation scenario in the test sequence after a predetermined time, includes:

[0057] If the previous actual emission source simulation scenario is an electrified mine dust emission scenario, the carbon dioxide flue gas emission monitoring standard measurement device is controlled to provide corresponding test parameters to provide desulfurization-related emission scenarios, and then switched to the next actual emission source simulation scenario in the test sequence after a predetermined time.

[0058] Understandably, based on the aforementioned verification method using multi-emission source simulation scenarios, the contamination of the test window can be further considered, and a dynamic switching mechanism between contaminated and clean scenarios can be introduced to improve the stability and measurement accuracy of the verification process. Traditional carbon dioxide flue gas emission monitoring standard measuring devices are prone to contamination of the test window after long-term operation due to particulate matter deposition, electrostatic adsorption, tar or water vapor condensation, leading to increased measurement errors. This method establishes a pollution risk level assessment mechanism, dividing different emission source simulation scenarios into contaminated and clean test window scenarios. In cases of severe test window contamination, an automatic cleaning scenario is used to self-clean the test window, restoring the optical transmittance of the measurement environment and improving the stability of long-term measurements.

[0059] For example, emission characteristics vary significantly across different industries during industrial production. Therefore, it is essential to define multiple emission source simulation scenarios based on the target industry's operating conditions to ensure that standard measuring devices can calibrate carbon dioxide concentration measuring instruments under various possible environmental conditions. For instance, in the thermal power generation industry, boiler combustion emissions typically contain 10%–14% carbon dioxide and 10–50 mg / m³. 3 Fly ash particles typically have a temperature of 80–120℃ and low humidity. In contrast, the CO2 concentration in cement plant clinker calcination processes is usually higher, reaching 14%–18%, and the emitted dust is mainly alkaline calcium oxide particles, with a temperature range of 100–180℃ and low humidity. In the exhaust gas from steel sintering machines, the CO2 concentration is 8%–12%, and the emitted particulate matter is mainly metal oxides (Fe2O3), with concentrations reaching 20–50 mg / m³. 3 The temperature ranges from 80 to 160°C, with moderate humidity. Furthermore, in coke oven flue gas emissions from coking plants, CO2 concentrations are typically 5%–10%, accompanied by tar particles and high humidity (above 40%). These scenarios encompass typical industrial emission characteristics, providing a more realistic calibration environment for the measuring device. To ensure the test device parameters match different industrial scenarios, the corresponding carbon dioxide gas generation method and particulate matter aerosol generation method need to be determined for each simulation scenario. High-purity CO2 gas is used, and precise adjustment is achieved through a mass flow controller (MFC) to ensure the CO2 concentration in different scenarios matches actual operating conditions. For example, the CO2 concentration is set to 12% in the simulated coal-fired power plant scenario, while it is set to 16% in the cement plant simulation scenario. Particulate matter generation is achieved using Venturi injectors or pneumatic conveying systems to ensure uniform distribution of particles in the wind tunnel, and the particle concentration is precisely controlled by adjusting the injection pressure and particle supply rate. For example, in the steelmaking tail gas scenario, the Fe2O3 particle concentration is set to 20–50 mg / m³. 3In addition, the temperature and humidity control system is used to match the environmental conditions of different emission scenarios. For example, when simulating flue gas after wet desulfurization, the humidity is increased to more than 50% to test the response performance of the measuring instrument in a high humidity environment.

[0060] For example, after determining the parameters for each emission source simulation scenario, real-time adjustments are required through the automated control system of a standard measuring device to ensure the accuracy of the test parameters. This device employs a straight wind tunnel structure, providing a stable airflow environment and ensuring the comparison accuracy of the measuring instruments. First, the system controls the CO2 mass flow rate according to the set operating conditions; for example, in the coal-fired power plant simulation scenario, the CO2 concentration is set to 12%, while in the cement kiln simulation scenario, it is set to 16%. Subsequently, the particulate matter generation system is activated, and the aerosol concentration is controlled by adjusting the injection pressure and particulate matter supply; for example, in the coking plant simulation scenario, the tar particle concentration is set to 10–40 mg / m³. 3 The wind tunnel system adjusts the airflow velocity according to the characteristics of the simulated scenario. For example, in high-temperature emissions (such as combustion exhaust gas), the wind speed is set to 8–10 m / s, while in low-velocity emission sources (such as cement kiln exhaust gas), it is set to 3–5 m / s. In addition, the airflow velocity is monitored by an L-shaped Pitot tube and a laser Doppler velocimeter (LDV), and the wind speed is adjusted in real time by a PID control system to match the actual operating conditions.

[0061] For example, because industrial emissions vary across different stages of the production process, the testing sequence needs to be adjusted according to the actual emission patterns. For instance, in a thermal power plant testing process, the combustion emission stage (high-temperature fly ash) is simulated first, then the post-desulfurization emission stage (high humidity, low-temperature CO2) is switched to, and finally the post-dust removal purification emission stage (low particulate concentration CO2) is tested. In a cement plant, the kiln head emission (high temperature, high CO2) needs to be simulated first, then the particulate matter purification stage is entered, and finally the low-temperature, low-dust emission stage is entered. By dynamically adjusting the testing sequence, the measuring instruments can maintain high accuracy throughout the entire emission cycle.

[0062] For example, during multiple emission scenario switching processes, the emission characteristics of certain scenarios (such as charged mineral dust, high-humidity tar particles, etc.) may contaminate the test window. Therefore, this method further introduces a test window contamination risk assessment mechanism to automatically adjust the testing process to reduce contamination accumulation. First, based on the test data of different emission sources, the contamination risk level of the test window is calculated, for example: charged mineral dust (high risk), high-temperature dust (medium risk), wet flue gas (low risk), and clean air (no contamination). After detecting that the previous scenario is a high-pollution emission source (such as charged mineral dust), the system automatically switches to a clean test window scenario (such as wet flue gas after wet desulfurization) for a short test to use moisture to flush out particulate matter and reduce contamination accumulation. The system can set a cleaning time window (such as 10 minutes), during which a high-humidity, low-particle flow field is maintained in the wind tunnel to gradually remove pollutants, and then the system switches to the next test scenario. This method can significantly improve the stability of the measuring instrument during long-term operation and reduce the need for manual cleaning and maintenance.

[0063] For example, when the previous actual emission source simulation scenario is identified as a charged mineral dust emission scenario, based on the characteristics of the flue gas particles simulated in the scenario, such as fine particle size, high charge state, low flow rate and low humidity, the particles are prone to electrostatic adsorption and accumulation in the transparent test window area, which in turn causes a decrease in light transmittance and affects the laser measurement path. Therefore, it is determined to be a test window pollution scenario.

[0064] To address this pollution risk, following this scenario, by controlling the wind speed, humidity adjustment module, and aerosol generator of the carbon dioxide flue gas emission monitoring standard measurement device, a desulfurization-related emission scenario with relatively high humidity, medium particulate concentration, and enhanced turbulent airflow characteristics is loaded. Utilizing the high humidity conditions and turbulent airflow structure of this scenario in actual emissions, the residual pollution from the previous scenario is purged, diluted, or reversed and removed, thereby achieving a relative cleaning effect on the transparent test window.

[0065] After maintaining the desulfurization-related emission scenario for a set duration (e.g., 30 to 180 seconds), the system automatically switches to the next actual emission source simulation scenario in the test sequence. This balances the reproduction of the plant's actual emission cycle with the slow-release control of contamination accumulation within the viewing window. This sequence optimization strategy, through thorough analysis of the parameter conflict mechanisms between different simulation scenarios, achieves dynamic maintenance of the measurement stability of the traceability channel without introducing additional cleaning hardware.

[0066] In some examples, it also includes:

[0067] The pollution risk level of multiple actual emission source simulation scenarios is evaluated based on the test parameters of different emission source simulation scenarios, so as to divide the multiple actual emission source simulation scenarios into test window pollution scenarios and prevention scenarios associated with these scenarios based on the test window pollution risk level.

[0068] Before providing the test window pollution scenario according to the test sequence, the carbon dioxide flue gas emission monitoring standard measuring device is controlled to provide corresponding test parameters to provide the prevention scenario, and then switched to the test window pollution scenario after a predetermined time.

[0069] In some examples, the step of controlling the carbon dioxide flue gas emission monitoring standard measuring device to provide corresponding test parameters to provide the prevention scenario before providing the test window pollution scenario according to the test sequence, and then switching to the test window pollution scenario after a predetermined period of time, includes:

[0070] Before providing the charged mine dust emission scenario according to the test sequence, the carbon dioxide flue gas emission monitoring standard measuring device is controlled to provide corresponding test parameters to provide desulfurization-related emission scenarios, and then switched to the charged mine dust emission scenario after a predetermined time.

[0071] Understandably, to address the calibration needs of carbon dioxide flue gas emission monitoring standard measuring devices under different industrial emission scenarios, this paper proposes an optimized calibration method that comprehensively considers the diversity of emission conditions, test window pollution management, and pollution prevention. This method constructs multiple simulated scenarios of real emission sources and dynamically adjusts key parameters such as carbon dioxide concentration, particulate matter concentration, temperature, humidity, and flow rate according to the characteristics of each condition. This enables the measuring device to accurately simulate different industrial emission conditions and comprehensively calibrates the carbon dioxide measuring instrument. Furthermore, this method introduces a test window pollution risk assessment mechanism, dividing different emission scenarios into high-pollution scenarios and associated pollution prevention scenarios. A prevention stage is provided before entering high-pollution environments, allowing the test window to undergo pretreatment before entering high-pollution environments to reduce pollution accumulation and improve the stability of long-term measurements. This method mainly includes the following core steps: (1) Divide multiple actual emission source simulation scenarios based on industry emission characteristics to ensure that the working conditions of the test device cover different industrial environments; (2) Dynamically control the measurement device based on the parameters of different emission scenarios to accurately provide the carbon dioxide gas and aerosol characteristics required for each scenario; (3) Optimize the test sequence according to the factory emission characteristics and reasonably arrange the switching sequence between pollution scenarios and low-pollution or clean scenarios; (4) Provide a prevention scenario before the pollution scenario, and use suitable airflow or environmental conditions to reduce the risk of pollution in the test window and improve the long-term measurement stability.

[0072] For example, during multi-scenario testing, certain emission scenarios (such as charged mineral dust, high-temperature dust, and high-humidity tar particles) may contaminate the test window. Therefore, before entering these high-pollution scenarios, the system will prioritize switching to the associated prevention scenario and pre-treat the test window using appropriate airflow or environmental conditions. For instance, before entering a charged mineral dust scenario (such as tail gas from steelmaking), the system will first switch to a desulfurization-related emission scenario (such as flue gas after wet desulfurization) to form a wet film on the test window surface, reducing the probability of charged particle adsorption. Similarly, before entering a coking plant tar flue gas scenario, a low-dust, high-humidity environment can be simulated to reduce the possibility of oil film contamination. The system will set a prevention time window (such as 10 minutes) to maintain specific environmental conditions during this period before switching to the high-pollution scenario, ensuring that the measuring instrument remains in optimal working condition throughout the entire testing process.

[0073] For example, if the simulated emission scenario of the actual emission source to be provided according to the test sequence is identified as a charged mineral dust emission scenario, since this type of scenario simulates the release behavior of highly charged, high-concentration mineral dust in a low-humidity, low-flow-rate environment, particulate matter is prone to electrostatic adsorption in the transparent area of ​​the test window, thereby forming a deposition layer, causing a decrease in light transmittance and interfering with laser-type measurement paths. Therefore, it is judged as a high-risk test window contamination scenario. To reduce this type of contamination risk, before entering the charged mineral dust scenario, the carbon dioxide flue gas emission monitoring standard measurement device is controlled to load a prevention scenario with opposite parameter trends. This prevention scenario can be selected as a desulfurization-related emission scenario, which has the characteristics of high humidity, moderate flow rate, and low particle charge state. It can achieve pollution suppression in the following ways: using high-humidity gas to form a water film or wetting layer on the surface of the test window to reduce the adsorption tendency of subsequent electrostatic particles; using moderate flow rate to disturb the airflow to remove residual particles already present on the window surface; adjusting the distribution and dispersion of fine particulate matter in the flue gas to change the subsequent particulate matter deposition behavior. After maintaining the desulfurization-related prevention scenario for a set duration (e.g., 30 to 150 seconds) to ensure the formation of certain anti-pollution aerodynamic boundary conditions at the test window, the system automatically switches to the charged mineral dust emission scenario and continues simulation and verification according to the test sequence. This "pre-pollution prevention" strategy achieves early intervention and control of pollution accumulation at the test window without altering the overall testing process and simulation sequence. Compared to post-pollution cleanup methods, this approach is more proactive and controllable, avoiding calibration errors or interruptions in window maintenance due to pollution accumulation, thereby improving the measurement stability and traceability of the carbon dioxide solubility measuring instrument during long-term continuous calibration.

[0074] In some examples, it also includes:

[0075] High-precision image data of the same transparent window segment of the transparent test window are periodically acquired;

[0076] The high-precision image data acquired in two consecutive periods are analyzed. The frame difference method is used to compare the differences between the two image data to identify the contamination points that coexist in the two periods.

[0077] The contours of the contaminated areas are extracted by combining threshold segmentation with morphological operations;

[0078] Calculate the geometric features of the contaminated area, and based on the location of the contaminated area, calculate the laser path that may be affected by the laser tracing module.

[0079] In the event of an affected laser path, the calibration analysis of the instrument to be calibrated by the metrology traceability module is suspended. The metrology traceability module includes the LDV and TDLAS gas analyzers.

[0080] For example, based on the standard measurement device for carbon dioxide flue gas emission monitoring, a transparent test window pollution monitoring and laser source tracing path adjustment mechanism is further integrated to improve the long-term measurement stability of the metrological traceability module (including LDV and TDLAS gas analyzers). During the calibration of the standard measurement device, the test window may gradually become contaminated due to factors such as particulate matter aerosols, humidity changes, and temperature gradients, affecting the accuracy of laser measurements. This method uses periodic high-precision image acquisition, computer vision algorithms to analyze the contaminated area, identify contamination points on the test window, and calculate the potentially affected laser path based on the location of the contaminated area. When contamination may interfere with the measurement accuracy of LDV or TDLAS, the system will pause the calibration analysis of the instrument under calibration by the metrological traceability module to prevent contamination from causing errors in the measurement results. The core steps of this method include: (1) periodically acquiring high-precision image data of the transparent test window to monitor the accumulation of contaminants; (2) using the frame difference method to compare the image data before and after, identifying the contaminant points coexisting in the two periods, and eliminating short-term smoke and dust interference; (3) using threshold segmentation combined with morphological processing to extract the contaminant area and accurately delineate the contaminant outline; (4) calculating the geometric features of the contaminant area and its influence on the laser path to determine whether it interferes with the measurement of LDV or TDLAS; (5) when the laser path is affected, pausing the measurement analysis of the value traceability module to avoid erroneous measurement data affecting the instrument calibration results.

[0081] For example, to monitor the contamination status of the test window, the system is equipped with a high-precision industrial camera or macro camera, and images of the same transparent segment of the transparent test window are captured at fixed time intervals (e.g., every 10 minutes). The camera's installation position must ensure that its viewing angle is consistent with the laser paths of LDV and TDLAS, so that the acquired image data can be directly used for contamination identification. To ensure image clarity, the system employs an autofocus mechanism combined with uniform LED backlighting to improve the contrast of contaminants. After image acquisition, the images are stored in the data processing module and timestamped for subsequent time-series analysis.

[0082] For example, after acquiring two consecutive high-precision image data sets, the system uses the frame difference method for image comparison, calculating the pixel differences between the two sets of image data to filter out interference from short-term floating particles. Specifically, the frame difference method compares two images pixel by pixel and calculates the difference image:

[0083] D(x,y)=∣I t (x,y)-I t-1 (x,y)∣

[0084] Among them, I t (x,y) and I t-1 (x,y) represent the pixel values ​​of the current and previous image cycles, respectively, and D(x,y) represents the pixel difference between the two images. If the difference in a certain region remains large for a long period, that region is considered a static contamination region, rather than a short-term aerosol interference. Subsequently, the system calculates the contamination points coexisting within the two cycles, that is, the fixed contamination regions that exist simultaneously in both images, to exclude contaminants that have been temporarily attached but carried away by the airflow, thereby improving the reliability of the detection.

[0085] For example, to further identify contaminated areas, the system employs an adaptive threshold segmentation algorithm to extract these areas from the background. Specifically, it first performs grayscale processing, and then uses an image binarization algorithm to dynamically calculate the optimal segmentation threshold.

[0086]

[0087] in, This represents the inter-class variance between the foreground and background. The goal is to find a threshold T that maximizes the difference between the foreground and background, thereby accurately segmenting the contaminated area, representing the total variance. After threshold segmentation, the system applies morphological operations (dilation, erosion, and contour extraction) to smooth the edges of the contaminated area, remove noise interference, and generate accurate contaminated contour information. Finally, a binary mask of the contaminated area is output for further analysis of its impact on the laser path.

[0088] For example, after the contaminated area is extracted, the system calculates the geometric features of the contaminated area, including: area to determine the degree of contamination; shape to analyze the type of contamination, such as particle adhesion or oil film coverage; center coordinates to calculate the location of the contamination point; and a bounding box to determine the contamination range. Subsequently, the system matches the location information of the contaminated area with the laser measurement paths of LDV and TDLAS. The laser path can be represented as one or more straight lines. If the bounding box of the contaminated area intersects with this straight line, it indicates that the contaminant may affect the laser transmittance, thus affecting the measurement accuracy. For example, in LDV velocities, if the contaminated area is located at the transmission point of the laser beam, it may lead to signal attenuation or abnormal scattering; in TDLAS gas analysis, if the contaminant covers the laser path, it may lead to increased measurement error. Therefore, the system needs to take further measures to ensure measurement accuracy.

[0089] For example, when a contaminated area affects the laser path of LDV or TDLAS, the system automatically pauses the calibration analysis of the instrument under test by the measurement traceability module and activates an alarm mechanism to notify the operator to clean or adjust the measurement path. While pausing the measurement, if the measuring device supports laser angle adjustment, the laser path is adjusted to avoid the contaminated area. Triggering the airflow cleaning system: If the contamination is particulate matter deposits, the high-pressure airflow purging system can be activated to clean the test window. Entering the test window cleaning scenario: If the contamination is severe, the system can first switch to a high-humidity, low-dust emission environment (such as wet desulfurization flue gas) to clean the contaminants using moisture before resuming the measurement.

[0090] In some examples, it also includes

[0091] Determine a new measurement angle to ensure the laser avoids contaminated areas;

[0092] The incident angle of the measurement traceability module is shifted to avoid contamination points.

[0093] Understandably, when contamination is detected that may affect the laser transmission path of LDV or TDLAS, the system calculates a new measurement angle to allow the laser to avoid the contaminated area. Based on the current location information of the contaminated area, the minimum rotation angle θ that causes the laser path to deviate from the contaminated area is calculated. Here, Δy and Δx represent the vertical and horizontal offsets between the contaminated area and the original laser path, respectively. The incident angle of the LDV or TDLAS laser is adjusted using a high-precision motorized rotary platform or micromirror adjustment mechanism to control the laser path, ensuring it bypasses the contaminated area while maintaining measurement accuracy. After calculating the new measurement angle, the system uses a laser adjustment mechanism driven by a stepper motor or servo motor to slightly offset the laser incident angle of the LDV or TDLAS (typically within 3°) to ensure its transmission path avoids the contaminated point. The offset measurement angle is monitored in real time by the system, and combined with an optical feedback mechanism, ensures that the measurement accuracy is not affected by the angle adjustment. Furthermore, if the contamination is severe, the system can trigger an airflow cleaning system to purge the test window and restore optimal measurement conditions.

[0094] Please see Figure 2 One embodiment of the carbon dioxide flue gas emission monitoring standard measuring device in this application is used to calibrate carbon dioxide emission measuring instruments. The carbon dioxide flue gas emission monitoring standard measuring device is constructed in a straight wind tunnel configuration to generate carbon dioxide gas and particulate matter aerosols. The device also includes a value traceability module and a transparent test window at the measurement location. The carbon dioxide flue gas emission monitoring standard measuring device is characterized by including:

[0095] Unit 21 is used to divide multiple actual emission source simulation scenarios based on the actual emission characteristics of the target industry factory. The carbon dioxide gas generation parameters, particulate matter aerosol generation parameters, and environmental parameters are different in different emission source simulation scenarios.

[0096] Control unit 22 is used to control the carbon dioxide flue gas emission monitoring standard measuring device according to different actual emission source simulation scenarios to provide corresponding test parameters;

[0097] Test unit 23 is used to control the test sequence of multiple actual emission source simulation scenarios in combination with the actual emission patterns of the target industry factory, so as to calibrate the carbon dioxide emission measuring instrument.

[0098] In summary, the carbon dioxide flue gas emission monitoring and calibration device provided in this application divides multiple simulated emission source scenarios based on the actual emission characteristics of the target industry factory. The carbon dioxide gas generation parameters, particulate matter aerosol generation parameters, and environmental parameters differ in each simulated emission source scenario. The device controls the carbon dioxide flue gas emission monitoring standard measuring device according to the different simulated emission source scenarios to provide corresponding test parameters. Furthermore, it controls the test sequence of multiple simulated emission source scenarios in conjunction with the actual emission patterns of the target industry factory to calibrate the carbon dioxide emission measuring instrument. By simulating the emission characteristics of different factories and controlling parameters such as carbon dioxide concentration, particulate matter concentration, flow rate, temperature, and humidity according to real operating conditions, the measurement accuracy and stability of the carbon dioxide concentration measuring instrument can be more comprehensively evaluated and calibrated. This approach ensures the reliability of the measuring instrument under various industrial emission environments, thus overcoming the limitations of traditional calibration methods that are restricted to a single operating condition. By constructing multiple simulated emission source scenarios, dynamically controlling the parameters of the standard measuring device, and adjusting the test sequence according to the factory emission patterns, the adaptability and calibration accuracy of the carbon dioxide measuring instrument are comprehensively improved. Compared to traditional calibration methods under a single environment, this method can more realistically reflect industrial emission conditions, enabling measuring instruments to maintain high accuracy and stability in various complex environments, and providing a reliable standard measurement system for environmental monitoring, industrial emission control and laboratory testing.

[0099] like Figure 3 As shown, this application embodiment also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 320 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any of the above-described methods for carbon dioxide flue gas emission monitoring and calibration.

[0100] Based on the actual emission characteristics of the target industry factories, multiple simulation scenarios of actual emission sources are divided. The carbon dioxide gas generation parameters, particulate matter aerosol generation parameters, and environmental parameters are different in different simulation scenarios of emission sources.

[0101] The carbon dioxide flue gas emission monitoring standard measuring device is controlled according to different actual emission source simulation scenarios to provide corresponding test parameters;

[0102] By combining the actual emission patterns of target industry factories, the test sequence of multiple actual emission source simulation scenarios is controlled in order to calibrate the carbon dioxide emission measurement instrument.

[0103] Since the electronic device described in this embodiment is the device used to implement a carbon dioxide flue gas emission monitoring and calibration device in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application falls within the scope of protection of this application.

[0104] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1 Any of the implementation methods in the corresponding embodiments:

[0105] Based on the actual emission characteristics of the target industry factories, multiple simulation scenarios of actual emission sources are divided. The carbon dioxide gas generation parameters, particulate matter aerosol generation parameters, and environmental parameters are different in different simulation scenarios of emission sources.

[0106] The carbon dioxide flue gas emission monitoring standard measuring device is controlled according to different actual emission source simulation scenarios to provide corresponding test parameters;

[0107] By combining the actual emission patterns of target industry factories, the test sequence of multiple actual emission source simulation scenarios is controlled in order to calibrate the carbon dioxide emission measurement instrument.

[0108] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0109] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0111] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0112] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0113] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform actions such as... Figure 1 The corresponding embodiment describes the process for carbon dioxide flue gas emission monitoring and calibration.

[0114] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0115] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0117] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0118] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0119] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for monitoring and calibrating carbon dioxide flue gas emissions, characterized in that, A standard measuring device for monitoring carbon dioxide emissions is used to calibrate carbon dioxide emission measuring instruments. The device is constructed in a straight wind tunnel configuration to generate carbon dioxide gas and particulate matter aerosols. The device also includes a traceability module and a transparent test window at the measurement location. The method includes: Based on the actual emission characteristics of the target industry factories, multiple simulation scenarios of actual emission sources are divided. The carbon dioxide gas generation parameters, particulate matter aerosol generation parameters, and environmental parameters are different in different simulation scenarios of emission sources. The carbon dioxide flue gas emission monitoring standard measuring device is controlled according to different actual emission source simulation scenarios to provide corresponding test parameters; By combining the actual emission patterns of factories in the target industry, the test sequence of multiple simulated scenarios of actual emission sources is controlled in order to calibrate the carbon dioxide emission measurement instrument. Also includes: The pollution risk level of multiple actual emission source simulation scenarios is evaluated based on the test parameters of different emission source simulation scenarios, so as to divide the multiple actual emission source simulation scenarios into test window pollution scenarios and test window clean scenarios based on the test window pollution risk level. If the previous actual emission source simulation scenario is a charged mine dust emission scenario, the carbon dioxide flue gas emission monitoring standard measurement device is controlled to provide corresponding test parameters to provide desulfurization-related emission scenarios, and then switched to the next actual emission source simulation scenario in the test sequence after a predetermined time. Also includes: The pollution risk level of multiple actual emission source simulation scenarios is evaluated based on the test parameters of different emission source simulation scenarios, so as to divide the multiple actual emission source simulation scenarios into test window pollution scenarios and prevention scenarios associated with these scenarios based on the test window pollution risk level. Before providing the electrified dust emission scenario according to the test sequence, the carbon dioxide flue gas emission monitoring standard measuring device is controlled to provide corresponding test parameters to provide desulfurization-related emission scenarios, and then switched to the electrified dust emission scenario after a predetermined time.

2. The method as described in claim 1, characterized in that, Also includes: High-precision image data of the same transparent window segment of the transparent test window are periodically acquired; The high-precision image data acquired in two consecutive periods are analyzed. The frame difference method is used to compare the differences between the two image data to identify the contamination points that coexist in the two periods. The contours of the contaminated areas are extracted by combining threshold segmentation with morphological operations; Calculate the geometric features of the contaminated area, and based on the location of the contaminated area, calculate the laser path that may be affected by the laser tracing module. In the event of an affected laser path, the calibration analysis of the instrument to be calibrated by the metrology traceability module is suspended. The metrology traceability module includes the LDV and TDLAS gas analyzers.

3. The method as described in claim 2, characterized in that, Also includes Determine a new measurement angle to ensure the laser avoids contaminated areas; The incident angle of the measurement traceability module is shifted to avoid contamination points.

4. A standard measuring device for monitoring carbon dioxide flue gas emissions, characterized in that, The carbon dioxide emission monitoring standard measuring device is used to calibrate carbon dioxide emission measuring instruments. It is constructed in a straight wind tunnel configuration to generate carbon dioxide gas and particulate matter aerosols. The device also includes a traceability module and a transparent test window at the measurement location. The device employs the method described in any one of claims 1 to 3. The carbon dioxide emission monitoring standard measuring device comprises: The division unit is used to divide multiple actual emission source simulation scenarios based on the actual emission characteristics of the target industry factory. The carbon dioxide gas generation parameters, particulate matter aerosol generation parameters, and environmental parameters are different in different emission source simulation scenarios. The control unit is used to control the carbon dioxide flue gas emission monitoring standard measuring device according to different actual emission source simulation scenarios to provide corresponding test parameters; The testing unit is used to control the testing sequence of multiple simulated emission source scenarios based on the actual emission patterns of factories in the target industry, in order to calibrate the carbon dioxide emission measuring instrument.

5. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program stored in the memory to implement the steps of the carbon dioxide flue gas emission monitoring and calibration method as described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the carbon dioxide flue gas emission monitoring and calibration method as described in any one of claims 1-3.

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