Thermal power generation carbon emission monitoring system and method

Abstract

The present invention discloses a carbon emission monitoring system and method for thermal power generation, and specifically relates to the technical field of carbon emission monitoring, and is used to solve the existing problem that the accuracy of the carbon emission output by a flue gas analyzer cannot be judged in advance; by analyzing the static pressure deviation and fluctuation during the flue gas flow process, the abnormality and hidden dangers are evaluated, and the normal external monitoring conditions are ensured; when the external monitoring conditions are normal, the hidden danger degree of the flue gas fluidity is evaluated by monitoring the volatility of the static pressure during the flue gas flow process; by comprehensively analyzing the flue gas fluidity, the influence of particulate matter and the sampling efficiency, the hidden danger degree of the operation of the flue gas analyzer is judged, the accuracy and reliability of the carbon emission monitoring data are ensured, the precision and stability of the monitoring system are improved, the data deviation is reduced, and support is provided for the optimization of power plants.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission monitoring, and more specifically, to a system and method for monitoring carbon emission in thermal power generation. Background Art

[0002] Thermal power generation is one of the main sources of global electricity supply, but it is also one of the important sources of carbon emissions. Most existing thermal power plants rely on fossil fuels such as coal, oil and natural gas, and the combustion process of these fuels will produce a large amount of carbon dioxide, which has a serious impact on the environment.

[0003] Carbon emission monitoring of thermal power generation usually uses flue gas analyzers, which perform flue gas sampling, flue gas composition measurement and data processing on carbon emissions of thermal power generation, and finally obtain carbon emissions. In the process of carbon emission monitoring by flue gas analyzers, the existing technology usually directly obtains the carbon emissions output by the flue gas analyzer to determine whether the carbon emissions are normal.

[0004] However, if the potential accuracy risks of the flue gas analyzer during carbon emission monitoring cannot be discovered in time, and the accuracy of the carbon emissions output by the flue gas analyzer cannot be judged in advance, it may lead to carbon emission data deviations, misleading emission control and environmental management decisions, and thus failing to effectively meet environmental requirements, leading to increased environmental pollution.

[0005] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a carbon emission monitoring system and method for thermal power generation to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The method for monitoring carbon emissions from thermal power generation comprises the following steps:

[0009] S1: Analyze the deviation of the static pressure of the gas during the smoke flow process and evaluate the abnormal degree of the static pressure of the gas during the smoke flow process;

[0010] S2: Determine whether the external conditions for carbon emission monitoring are normal based on the abnormal degree of gas static pressure during flue gas flow; when the external conditions for carbon emission monitoring are normal, evaluate the hidden danger of flue gas fluidity by monitoring the volatility of static pressure during flue gas flow;

[0011] S3: By analyzing the size and density of particles passing through the flue gas analyzer, the potential hazards of particles to carbon emission monitoring are evaluated;

[0012] S4: Analyze the sampling performance of the sampling probe and evaluate the stability of the sampling efficiency of carbon emission monitoring;

[0013] S5: Comprehensively analyze the potential risks of flue gas fluidity, the potential risks of particulate matter to carbon emission monitoring, and the stability of sampling efficiency, evaluate the potential risks of flue gas analyzer operation, and determine the accuracy of carbon emission data based on the potential risks of flue gas analyzer operation.

[0014] In a preferred embodiment, in S1, the deviation of the static pressure of the gas during the flow of the smoke is analyzed to evaluate the abnormal degree of the static pressure of the gas during the flow of the smoke, specifically:

[0015] Obtain static pressure and ambient pressure: static pressure is the pressure of flue gas in the flue, and ambient pressure is the atmospheric pressure of the environment where the flue gas analyzer is located;

[0016] Calculate the static pressure judgment value: Among them, Ess is the static pressure judgment value, P is the static pressure, P 0 For environmental pressure.

[0017] In a preferred embodiment, in S2, whether the external conditions for carbon emission monitoring are normal is determined according to the abnormal degree of gas static pressure during flue gas flow, specifically:

[0018] Set a static pressure judgment threshold; when the static pressure judgment value is greater than the static pressure judgment threshold, it is determined that the gas static pressure during the flue gas flow is abnormal; when the static pressure judgment value is less than or equal to the static pressure judgment threshold, it is determined that the gas static pressure during the flue gas flow is normal;

[0019] When the static pressure of gas during flue gas flow is abnormal, the external conditions for carbon emission monitoring are determined to be abnormal; when the static pressure of gas during flue gas flow is normal, the external conditions for carbon emission monitoring are determined to be normal.

[0020] In a preferred embodiment, when the external conditions for carbon emission monitoring are normal, the potential risk of flue gas flow is evaluated by monitoring the volatility of static pressure during flue gas flow:

[0021] A smoke flow monitoring set is set, which includes multiple static pressure judgment values ​​closest to the real time. The static pressure judgment values ​​of the smoke flow monitoring set are numbered in chronological order. The standard deviation of the static pressure judgment values ​​in the smoke flow monitoring set is calculated by analyzing the volatility of the static pressure judgment values ​​in the smoke flow monitoring set. The expression is: Among them, σ Essis the standard deviation of the static pressure judgment value in the smoke flow monitoring set, M is the number of static pressure judgment values ​​in the smoke flow monitoring set, u is the number of static pressure judgment values ​​in the smoke flow monitoring set, M is a positive integer, u=1, 2, 3, ..., M, Ess u is the uth static pressure judgment value in the flue gas flow monitoring set, μ Ess It is the average value of the static pressure judgment value in the smoke flow monitoring set;

[0022] The ratio of the standard deviation of the static pressure judgment values ​​in the smoke flow monitoring set to the average value of the static pressure judgment values ​​in the smoke flow monitoring set is marked as the static pressure variation index.

[0023] In a preferred embodiment, in S3, the size and density of the particles passing through the flue gas analyzer are analyzed to evaluate the potential risk of the particles to carbon emission monitoring, specifically:

[0024] Collect particulate matter samples from flue gas emission sources and obtain particle diameter, particle density and particle concentration;

[0025] Calculate the comprehensive evaluation index of particle characteristics, the expression is: Where Efs is the comprehensive evaluation index of particle characteristics, f(D, ρ, C) is the function of the effect of a single particle on carbon measurement, f(D, ρ, C) = D 2 *ρ*log(C+1),μ f is the average value of f(D, ρ, C) of all measured particles, D i is the particle diameter of the i-th particle, ρ i is the particle density of the i-th particle, C i is the particle concentration of the i-th particle, α and β are the weight factors of the mean and variance of the values ​​generated by the particle characteristic function f(D, ρ, C), i is the index of the particle, N is the total number of particles, N is a positive integer, i = 1, 2, 3, ..., N.

[0026] In a preferred embodiment, in S4, the sampling performance of the sampling probe is analyzed to evaluate the stability of the carbon emission monitoring sampling efficiency, specifically:

[0027] Real-time acquisition of the sampling probe's command extraction flow rate; real-time monitoring of the flue gas flow rate actually extracted by the sampling probe;

[0028] Monitor multiple actual extracted flue gas flow rates closest to the real time in real time, obtain the command extraction flow rate at the time point corresponding to the actual extracted flue gas flow rate, analyze the degree of deviation between the actual extracted flue gas flow rate and the command extraction flow rate, and calculate the extraction efficiency abnormality index, which is expressed as: Among them, Cy ss is the extraction efficiency abnormality index, q is the number of the actual extracted flue gas flow rate monitored closest to the real time, k is the number of the actual extracted flue gas flow rate monitored closest to the real time, q = 1, 2, 3, ..., k, k is a positive integer, Sv q is the actual flue gas velocity extracted at the qth time, mv q is the command extraction flow rate at the time point corresponding to the qth actually extracted flue gas flow rate.

[0029] In a preferred embodiment, in S5, the potential risk level of flue gas fluidity, the potential risk level of particulate matter to carbon emission monitoring, and the stability of sampling efficiency are comprehensively analyzed to evaluate the potential risk level of the flue gas analyzer operation, specifically:

[0030] The static pressure variation index, the comprehensive evaluation index of particulate matter characteristics and the extraction efficiency abnormality index are normalized, and the normalized static pressure variation index, the comprehensive evaluation index of particulate matter characteristics and the extraction efficiency abnormality index are respectively assigned preset proportional coefficients, and the carbon emission monitoring accurate evaluation coefficient is calculated. The degree of operational hidden dangers of the flue gas analyzer is evaluated through the carbon emission monitoring accurate evaluation coefficient;

[0031] Determine the accuracy of carbon emission data based on the degree of operational risks of the flue gas analyzer:

[0032] Setting a first threshold for accurate assessment of carbon emission monitoring and a second threshold for accurate assessment of carbon emission monitoring, wherein the first threshold for accurate assessment of carbon emission monitoring is less than the second threshold for accurate assessment of carbon emission monitoring;

[0033] The carbon emission monitoring accuracy assessment coefficient is compared with the first threshold of carbon emission monitoring accuracy assessment and the second threshold of carbon emission monitoring accuracy assessment, specifically:

[0034] When the carbon emission monitoring accuracy assessment coefficient is less than the first threshold of the carbon emission monitoring accuracy assessment, it is determined that a carbon emission monitoring accuracy low risk signal is generated;

[0035] When the carbon emission monitoring accuracy assessment coefficient is greater than or equal to the first threshold of the carbon emission monitoring accuracy assessment, and the carbon emission monitoring accuracy assessment coefficient is less than or equal to the second threshold of the carbon emission monitoring accuracy assessment, it is determined that a carbon emission monitoring accuracy medium risk signal is generated;

[0036] When the carbon emission monitoring accuracy assessment coefficient is greater than the carbon emission monitoring accuracy assessment second threshold, it is determined that a carbon emission monitoring accuracy high risk signal is generated.

[0037] On the other hand, the present invention provides a thermal power generation carbon emission monitoring system, including a static pressure anomaly assessment module, an external condition judgment module, a static pressure fluctuation assessment module, a particle hidden danger assessment module, a sampling efficiency assessment module and a carbon emission credibility assessment module;

[0038] The static pressure anomaly assessment module analyzes the deviation of the static pressure of the gas during the smoke flow process and assesses the degree of abnormality of the static pressure of the gas during the smoke flow process;

[0039] The external condition judgment module determines whether the external conditions for carbon emission monitoring are normal based on the abnormal degree of gas static pressure during flue gas flow;

[0040] When the external conditions of carbon emission monitoring are normal, the static pressure fluctuation assessment module evaluates the hidden dangers of flue gas flow by monitoring the fluctuation of static pressure during flue gas flow;

[0041] The particle hazard assessment module analyzes the size and density of particles passing through the flue gas analyzer to assess the potential hazard of particles to carbon emission monitoring.

[0042] The sampling efficiency evaluation module analyzes the sampling performance of the sampling probe and evaluates the stability of the carbon emission monitoring sampling efficiency;

[0043] The carbon emission credible assessment module conducts a comprehensive analysis of the potential risk level of flue gas fluidity, the potential risk level of particulate matter to carbon emission monitoring, and the stability of sampling efficiency, evaluates the potential risk level of the flue gas analyzer's operation, and determines the accuracy of the carbon emission data based on the potential risk level of the flue gas analyzer's operation.

[0044] The technical effects and advantages of the thermal power generation carbon emission monitoring system and method of the present invention are as follows:

[0045] 1. By analyzing the static pressure deviation and fluctuation during the flue gas flow process, the abnormality and hidden dangers are evaluated to ensure the normal external monitoring conditions. When the external monitoring conditions are normal, the volatility of the static pressure during the flue gas flow process is monitored to evaluate the hidden dangers of the flue gas fluidity and ensure the normal external monitoring conditions, thereby improving the accuracy and stability of the flue gas fluidity monitoring and ensuring the reliability of the data.

[0046] 2. Quantify the potential impact of particle size and density on monitoring results. Evaluate the stability of sampling efficiency through performance analysis of the sampling probe. Finally, calculate the accurate assessment coefficient of carbon emission monitoring through comprehensive analysis of flue gas fluidity, particulate impact and sampling efficiency, and then judge the degree of operational risks of the flue gas analyzer. This method can monitor and warn of potential problems in real time, ensuring the accuracy and reliability of carbon emission monitoring data. In terms of technical effects, the present invention improves the accuracy and stability of the monitoring system, reduces data deviations, ensures the compliance and environmental friendliness of carbon emissions from thermal power generation, and provides support for power plant optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic diagram of the carbon emission monitoring method for thermal power generation according to the present invention;

[0048] Figure 2 It is a structural schematic diagram of the carbon emission monitoring system for thermal power generation according to the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Example 1

[0051] Figure 1 The present invention provides a method for monitoring carbon emissions from thermal power generation, which comprises the following steps:

[0052] S1: Analyze the deviation of the static pressure of the gas during the flue gas flow process and evaluate the abnormal degree of the static pressure of the gas during the flue gas flow process.

[0053] S2: Determine whether the external conditions for carbon emission monitoring are normal based on the abnormal degree of gas static pressure during flue gas flow; when the external conditions for carbon emission monitoring are normal, evaluate the degree of hidden dangers of flue gas fluidity by monitoring the volatility of static pressure during flue gas flow.

[0054] S3: By analyzing the size and density of particulate matter passing through the flue gas analyzer, the potential hazard of particulate matter to carbon emission monitoring is evaluated.

[0055] S4: Analyze the sampling performance of the sampling probe and evaluate the stability of the carbon emission monitoring sampling efficiency.

[0056] S5: Comprehensively analyze the potential risks of flue gas fluidity, the potential risks of particulate matter to carbon emission monitoring, and the stability of sampling efficiency, evaluate the potential risks of flue gas analyzer operation, and determine the accuracy of carbon emission data based on the potential risks of flue gas analyzer operation.

[0057] In S1, the deviation of the static pressure of the gas during the flue gas flow is analyzed to evaluate the abnormal degree of the static pressure of the gas during the flue gas flow, specifically:

[0058] In thermal power plants, monitoring and analyzing the static pressure of gas during flue gas flow is an important step in ensuring that the flue gas analyzer can correctly measure carbon emissions. The static pressure of gas refers to the pressure of flue gas inside the flue when there is no flow. Monitoring whether it is within the normal range is of great significance for ensuring the accuracy of flue gas sampling, preventing equipment overload, and avoiding safety accidents.

[0059] Obtain static pressure and ambient pressure. Static pressure is the pressure of flue gas in the flue, usually in Pascal; ambient pressure is the atmospheric pressure of the environment where the flue gas analyzer is located, usually in Pascal. Specifically:

[0060] Static pressure is obtained through pressure sensors installed in the flue. These sensors need to be calibrated regularly to ensure the accuracy of the data.

[0061] Ambient pressure is obtained by measuring the atmospheric pressure using a barometer located outside the power station to refer to local meteorological conditions.

[0062] In order to evaluate whether the static pressure during the flue gas flow is normal, the static pressure judgment value is calculated: Among them, Ess is the static pressure judgment value, P is the measured flue gas static pressure, P 0 For environmental pressure.

[0063] The larger the static pressure judgment value is, the greater the abnormality of the gas static pressure during the flue gas flow process.

[0064] In S2, according to the abnormal degree of gas static pressure during flue gas flow, it is determined whether the external conditions for carbon emission monitoring are normal. When the external conditions for carbon emission monitoring are normal, the hidden danger degree of flue gas fluidity is evaluated by monitoring the volatility of static pressure during flue gas flow, specifically:

[0065] The static pressure judgment threshold is set. The static pressure judgment threshold is set by professional and technical personnel in this field based on the size of the static pressure judgment value and other actual conditions such as the safety requirements for gas static pressure in practice.

[0066] When the static pressure judgment value is greater than the static pressure judgment threshold, it is determined that the static pressure of the gas during the flue gas flow process is abnormal.

[0067] When the static pressure judgment value is less than or equal to the static pressure judgment threshold, it is determined that the static pressure of the gas during the flue gas flow process is normal.

[0068] When the static pressure of gas during flue gas flow is abnormal, the external conditions for carbon emission monitoring are determined to be abnormal.

[0069] When the static pressure of the gas during the flue gas flow is normal, the external conditions for carbon emission monitoring are determined to be normal.

[0070] When the external conditions for carbon emission monitoring are determined to be abnormal, the following measures can be taken:

[0071] Perform detailed inspection and testing of the flue gas analyzer and its system components to identify the specific cause of the anomaly.

[0072] Check the system for filtering particles in the flue gas for problems, such as clogged or damaged filters.

[0073] If abnormal static pressure is found, it is necessary to immediately calibrate the pressure sensor or repair the mechanical problem that may cause the erroneous reading.

[0074] If the sensor is affected by particulate buildup, it may be necessary to replace or clean the sensor.

[0075] Based on the results of fault diagnosis, evaluate whether the monitoring system needs to be technically upgraded or old components need to be replaced to improve the reliability and accuracy of the system.

[0076] Through this systematic approach, the external conditions for carbon emission monitoring in thermal power plants can be evaluated and guaranteed more accurately, ensuring the reliability of data and the effectiveness of monitoring.

[0077] When the external conditions of carbon emission monitoring are normal, a flue gas flow monitoring set is set. The flue gas flow monitoring set includes multiple static pressure judgment values ​​closest to the real time. The static pressure judgment values ​​of the flue gas flow monitoring set are numbered in chronological order. The standard deviation of the static pressure judgment values ​​in the flue gas flow monitoring set is calculated by analyzing the volatility of the static pressure judgment values ​​in the flue gas flow monitoring set. The expression is: Among them, σ Ess is the standard deviation of the static pressure judgment value in the smoke flow monitoring set, M is the number of static pressure judgment values ​​in the smoke flow monitoring set, u is the number of static pressure judgment values ​​in the smoke flow monitoring set, M is a positive integer, u=1, 2, 3, ..., M, Ess u is the uth static pressure judgment value in the flue gas flow monitoring set, μEss It is the average value of the static pressure judgment value in the smoke flow monitoring set.

[0078] The larger the standard deviation of the static pressure judgment value within the smoke flow monitoring set, the greater the volatility of the static pressure judgment value, and the higher the hidden danger of smoke flowability.

[0079] The ratio of the standard deviation of the static pressure judgment value in the smoke flow monitoring set to the average value of the static pressure judgment value in the smoke flow monitoring set is marked as the static pressure variation index. The larger the static pressure variation index is, the higher the hidden danger of smoke flowability is.

[0080] By setting up a flue gas flow monitoring set, numbering and analyzing the static pressure judgment values ​​in the set, and calculating the standard deviation and coefficient of variation of the static pressure judgment values, the hidden danger level of flue gas flow can be systematically evaluated. The main benefit of this method is that it can provide a quantitative and real-time evaluation mechanism to help operators identify and respond to potential operating problems in a timely manner. The larger the standard deviation, the greater the fluctuation of the static pressure, reflecting the instability of the system and the increase in the degree of hidden dangers; the static pressure variation index further standardizes this volatility, allowing it to be compared relative to the average value, providing a more intuitive risk assessment indicator. By continuously monitoring and calculating these indicators, operators can understand the operating status of the system in real time, quickly discover anomalies, and take necessary adjustments and maintenance measures, thereby improving the reliability of the flue gas monitoring system and the accuracy of the data, and ensuring the carbon emission monitoring and environmental compliance of thermal power plants.

[0081] In S3, the size and density of the particles passing through the flue gas analyzer are analyzed to assess the potential hazards of the particles to carbon emission monitoring, specifically:

[0082] In the flue gas emission monitoring of thermal power plants, the size and density of particulate matter are important factors affecting the accuracy of flue gas analyzers. Particulate matter may affect the composition of gas samples, thereby affecting the measurement of greenhouse gases such as carbon dioxide. It is crucial to analyze the potential hidden dangers of particulate matter in carbon emission monitoring, because the presence of particulate matter may significantly affect the measurement accuracy and stability of flue gas analyzers. Particulate matter may cause physical interference to the sensor, contaminate the sensor surface, and cause deviations in the measurement results. In addition, the influence of different particle densities and sizes on light scattering and absorption characteristics may also interfere with the detection of gas composition. These problems will directly affect the accuracy of carbon dioxide emission data, and thus affect the environmental compliance and emission control strategies of thermal power plants. Therefore, systematically evaluating and analyzing the impact of particulate matter will help improve the reliability of monitoring equipment and the accuracy of data, and ensure that the carbon emission monitoring results are authentic and reliable.

[0083] Collect particulate matter samples from flue gas emission sources and obtain the particle diameter, which represents the average diameter of the particles in micrometers (μm). The particle diameter is usually measured by a laser scattering particle size analyzer.

[0084] Get the particle density, which indicates the density of the particles in grams per cubic centimeter (g / cm 3 ). Particle density is usually determined from the density of a known substance or by measurement techniques such as a gas pycnometer.

[0085] Get the particle concentration, which is the concentration of particulate matter in the flue gas, and its unit is milligrams per cubic meter (mg / m 3 ). The particle concentration is determined by sampling the flue gas and performing mass analysis.

[0086] To evaluate the characteristics of particulate matter, it is necessary to consider its potential impact on the measurement results of the flue gas analyzer and calculate the comprehensive evaluation index of particulate matter characteristics, which is expressed as: Where Efs is the comprehensive evaluation index of particle characteristics, f(D, ρ, C) is the function of the effect of a single particle on carbon measurement, f(D, ρ, C) = D 2 *ρ*log(C+1),μ f is the average value of f(D, ρ, C) of all measured particles.

[0087] D 2 represents the effect of the surface area of ​​the particles. Larger particles may more easily affect the fluidity of the smoke and the readings of the sensor. ρ takes into account the density of the particles. Denser particles may have a greater physical impact on the sensor. log(C+1) is a smoothing treatment of the particle concentration, ensuring that the function has moderate sensitivity when the concentration is low, while avoiding the undefined problem of logarithms in mathematical processing.

[0088] Among them, D i is the particle diameter of the i-th particle, ρ i is the particle density of the i-th particle, C i is the particle concentration of the ith particle.

[0089] α and β are weight factors of the mean and variance of the values ​​generated by the particle characteristic function f(D, ρ, C), respectively. α and β are adjusted according to experiments or actual operations to adjust the influence of the mean and variance of the values ​​generated by the particle characteristic function f(D, ρ, C) in the total score.

[0090] i is the index of the particle, which is used to indicate the particle currently being processed. In a data set, each particle is identified by a unique index i, which is used to traverse all particles. For example, when calculating the average value of the influence function of all particles, i varies from 1 to N.

[0091] N is the total number of particles, N is a positive integer, i=1, 2, 3,...,N.

[0092] The larger the comprehensive evaluation index of particulate matter characteristics, the higher the diameter, density and concentration of the particulate matter, which means that the particulate matter may cause significant interference to the measurement of the flue gas analyzer, that is, the higher the potential hidden dangers of particulate matter to carbon emission monitoring, which may lead to inaccurate measurement data or equipment failure.

[0093] In S4, the sampling performance of the sampling probe is analyzed to evaluate the stability of the carbon emission monitoring sampling efficiency, specifically:

[0094] The sampling probe plays a vital role in the carbon emission monitoring system of thermal power generation. Its function is to extract a part of the flue gas samples from the airflow in the flue, and then transport these samples to the flue gas analyzer for component analysis.

[0095] The sampling probe tube is inserted into the flue, usually in the center of the flue cross section or a representative flue gas flow position. This ensures that the sampling point can obtain a uniform and representative flue gas sample. The sampling pump is started to generate negative pressure to draw the flue gas from the flue into the sampling probe tube. By adjusting the power of the sampling pump, the speed of extracting the flue gas can be controlled.

[0096] The command extraction flow rate of the sampling probe is obtained in real time. The command extraction flow rate is the speed of extracting flue gas set by the flue gas analyzer.

[0097] The flow rate of flue gas actually extracted by the sampling probe is monitored in real time by the flow meter.

[0098] If there is a deviation between the actual extracted flue gas flow rate and the commanded extraction flow rate of the sampling probe, it means that the sampling performance of the sampling probe is poor. The occasional large deviation between the actual extracted flue gas flow rate and the commanded extraction flow rate of the sampling probe is within an acceptable range. However, if there are frequent deviations between the actual extracted flue gas flow rate and the commanded extraction flow rate of the sampling probe in a short period of time, it may indicate that the stability of the carbon emission monitoring sampling efficiency is poor.

[0099] The actual extracted flue gas flow rates closest to the real time are monitored in real time, and the commanded extraction flow rates at the time points corresponding to the actual extracted flue gas flow rates are obtained. The extraction efficiency abnormality index is calculated by analyzing the degree of deviation between the actual extracted flue gas flow rates and the commanded extraction flow rates. The expression is: Among them, Cy ssis the extraction efficiency abnormality index, q is the number of the actual extracted flue gas flow rate monitored closest to the real time, k is the number of the actual extracted flue gas flow rate monitored closest to the real time, q = 1, 2, 3, ..., k, k is a positive integer, Sv q is the actual flue gas velocity extracted at the qth time, mv q is the command extraction flow rate at the time point corresponding to the qth actually extracted flue gas flow rate.

[0100] The larger the extraction efficiency abnormality index is, the greater the deviation between the actual extracted flue gas flow rate and the commanded extraction flow rate will be, which will result in worse stability of the carbon emission monitoring sampling efficiency.

[0101] In S5, the potential risk level of flue gas fluidity, the potential risk level of particulate matter to carbon emission monitoring, and the stability of sampling efficiency are comprehensively analyzed to evaluate the potential risk level of flue gas analyzer operation. The accuracy of carbon emission data is determined based on the potential risk level of flue gas analyzer operation. Specifically:

[0102] The static pressure variation index, the comprehensive evaluation index of particulate matter characteristics and the extraction efficiency abnormality index are normalized, and after the normalized static pressure variation index, the comprehensive evaluation index of particulate matter characteristics and the extraction efficiency abnormality index are assigned preset proportional coefficients respectively, the carbon emission monitoring accurate evaluation coefficient is calculated, and the degree of operational hidden dangers of the flue gas analyzer is evaluated by the carbon emission monitoring accurate evaluation coefficient. The specific implementation method of calculating the above-mentioned carbon emission monitoring accurate evaluation coefficient is not specifically limited here. Any method that can calculate the carbon emission monitoring accurate evaluation coefficient by assigning preset proportional coefficients to the normalized static pressure variation index, the comprehensive evaluation index of particulate matter characteristics and the extraction efficiency abnormality index is acceptable. In order to realize the technical solution of the present invention, the present invention provides a specific method for calculating the carbon emission monitoring accurate evaluation coefficient.

[0103] The expression of the carbon emission monitoring accuracy assessment coefficient is: Among them, Tj is the carbon emission monitoring accuracy assessment coefficient, Jyb is the static pressure variation index, θ 1 ,θ 2 ,θ 3 are the preset proportional coefficients of the static pressure variation index, the comprehensive evaluation index of particle characteristics and the extraction efficiency abnormality index, and θ 1 ,θ 2 ,θ 3 Both are greater than 0.

[0104] The preset proportionality factor should be set according to the degree of influence of each indicator on the accuracy of the flue gas analyzer.

[0105] Specifically:

[0106] Static pressure variation index: reflects the stability of smoke flow. Pressure variation may lead to instability of sensor data and affect the accuracy of the entire system.

[0107] Particle characteristics comprehensive evaluation index: Particles may affect the measurement accuracy of the sensor and the long-term stability of the equipment. High concentrations of particles will interfere with the measurement of gas composition.

[0108] Extraction efficiency anomaly index: directly reflects the performance of the sampling probe and the representativeness of the sampling. Fluctuations in sampling efficiency can lead to deviations in sample data.

[0109] Use statistical analysis methods, such as multiple regression analysis, to analyze the actual impact of each parameter on monitoring accuracy through historical data and calculate the optimal preset proportional coefficient.

[0110] It can be seen from the calculation formula of the carbon emission monitoring accuracy assessment coefficient that the larger the carbon emission monitoring accuracy assessment coefficient, the greater the degree of operational hidden dangers of the flue gas analyzer. The larger the carbon emission monitoring accuracy assessment coefficient, the more obvious the problems such as static pressure variation, particulate matter influence and extraction efficiency instability are during the monitoring process. Each sub-index (static pressure variation index, particulate matter characteristic comprehensive assessment index and extraction efficiency abnormality index) reflects the potential hidden dangers of the flue gas analyzer in different aspects. After normalization and weighted processing, a larger assessment coefficient means that the combined impact of these hidden dangers is greater, which may lead to a decrease in the accuracy of sensor measurement, an increase in data deviation and unstable equipment operation. Therefore, the larger the carbon emission monitoring accuracy assessment coefficient, the greater the degree of operational hidden dangers of the flue gas analyzer, indicating that the reliability and accuracy of the monitoring data are reduced.

[0111] Determine the accuracy of carbon emission data based on the degree of operational risks of the flue gas analyzer:

[0112] A first threshold for accurate assessment of carbon emission monitoring and a second threshold for accurate assessment of carbon emission monitoring are set, wherein the first threshold for accurate assessment of carbon emission monitoring is less than the second threshold for accurate assessment of carbon emission monitoring.

[0113] The first threshold for accurate assessment of carbon emission monitoring and the second threshold for accurate assessment of carbon emission monitoring are set by professional and technical personnel in this field based on the size of the accurate assessment coefficient of carbon emission monitoring and other actual conditions such as the actual requirements for the accuracy of carbon emission monitoring, and will not be repeated here.

[0114] The carbon emission monitoring accuracy assessment coefficient is compared with the first threshold of carbon emission monitoring accuracy assessment and the second threshold of carbon emission monitoring accuracy assessment, specifically:

[0115] When the carbon emission monitoring accuracy assessment coefficient is less than the first threshold of the carbon emission monitoring accuracy assessment, a carbon emission monitoring accuracy low risk signal is generated. At this time, the operating risk level of the flue gas analyzer is not or is within an acceptable range, and the accuracy of the carbon emission data monitored by the flue gas analyzer is normal.

[0116] When the carbon emission monitoring accuracy assessment coefficient is greater than or equal to the first threshold of carbon emission monitoring accuracy assessment, and the carbon emission monitoring accuracy assessment coefficient is less than or equal to the second threshold of carbon emission monitoring accuracy assessment, it is determined that a carbon emission monitoring accuracy medium risk signal is generated. At this time, static pressure variation, particulate matter influence and extraction efficiency fluctuations begin to appear, but have not yet reached a serious level. There are potential risks in the accuracy of the carbon emission data monitored by the flue gas analyzer.

[0117] Measures taken include:

[0118] Strengthen monitoring: Increase monitoring frequency and conduct more intensive monitoring of key parameters to ensure that problems are discovered in a timely manner.

[0119] Adjust operations: Based on the monitoring data, appropriately adjust operating parameters, such as adjusting the power of the sampling pump, optimizing the use of filters, etc., to reduce fluctuations.

[0120] Key inspection: Carry out key inspection and maintenance on the links where fluctuations occur to eliminate hidden dangers in a timely manner.

[0121] When the carbon emission monitoring accuracy assessment coefficient is greater than the second threshold of carbon emission monitoring accuracy assessment, a high-risk signal for carbon emission monitoring accuracy is generated, indicating that the operating risk level of the flue gas analyzer is within the high-risk range. At this time, the static pressure variation, particulate matter impact and extraction efficiency fluctuations are significant, which may lead to a significant decrease in the accuracy of the monitoring data. Immediate measures need to be taken to restore the normal operation of the equipment.

[0122] Measures taken include:

[0123] Emergency inspection and maintenance: Immediately conduct a comprehensive inspection and maintenance of the flue gas analyzer, especially a detailed inspection of key components such as sensors, sampling probes, and pretreatment systems.

[0124] Data calibration: Calibrate monitoring data to ensure the accuracy of current data, and review historical data to find the root cause of deviations.

[0125] Equipment replacement: If inspection finds that certain components have failed or are close to failure, they must be replaced in a timely manner to restore the normal function of the system.

[0126] Improve the warning level: Increase the system warning level to ensure that operators can receive alarms in time and take corresponding measures.

[0127] Example 2

[0128] The difference between Example 2 of the present invention and Example 1 is that this example introduces a carbon emission monitoring system for thermal power generation.

[0129] Figure 2 A structural schematic diagram of the thermal power generation carbon emission monitoring system of the present invention is given, which includes a static pressure anomaly assessment module, an external condition judgment module, a static pressure fluctuation assessment module, a particle hidden danger assessment module, a sampling efficiency assessment module and a carbon emission credibility assessment module.

[0130] The static pressure anomaly assessment module analyzes the deviation of the static pressure of the gas during the flue gas flow process and assesses the degree of abnormality of the static pressure of the gas during the flue gas flow process.

[0131] The external condition judgment module determines whether the external conditions for carbon emission monitoring are normal according to the abnormal degree of gas static pressure during flue gas flow.

[0132] When the external conditions for carbon emission monitoring are normal, the static pressure fluctuation assessment module evaluates the hidden dangers of flue gas fluidity by monitoring the fluctuation of static pressure during flue gas flow.

[0133] The particle hazard assessment module analyzes the size and density of particles passing through the flue gas analyzer to assess the potential hazard of particles to carbon emission monitoring.

[0134] The sampling efficiency evaluation module analyzes the sampling performance of the sampling probe and evaluates the stability of the carbon emission monitoring sampling efficiency.

[0135] The carbon emission credible assessment module conducts a comprehensive analysis of the potential risk level of flue gas fluidity, the potential risk level of particulate matter to carbon emission monitoring, and the stability of sampling efficiency, evaluates the potential risk level of the flue gas analyzer's operation, and determines the accuracy of the carbon emission data based on the potential risk level of the flue gas analyzer's operation.

[0136] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0137] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0138] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0139] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0140] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0141] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0143] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0144] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0145] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring carbon emissions from thermal power generation, characterized in that: The steps include: S1: Analyze the deviation of the static pressure of the gas during the smoke flow process and evaluate the abnormal degree of the static pressure of the gas during the smoke flow process; S2: Determine whether the external conditions for carbon emission monitoring are normal based on the abnormal degree of gas static pressure during flue gas flow; when the external conditions for carbon emission monitoring are normal, evaluate the hidden danger of flue gas fluidity by monitoring the volatility of static pressure during flue gas flow; S3: By analyzing the size and density of particles passing through the flue gas analyzer, the potential hazards of particles to carbon emission monitoring are evaluated; S4: Analyze the sampling performance of the sampling probe and evaluate the stability of the sampling efficiency of carbon emission monitoring; S5: Comprehensively analyze the potential risks of flue gas fluidity, the potential risks of particulate matter to carbon emission monitoring, and the stability of sampling efficiency, evaluate the potential risks of flue gas analyzer operation, and judge the accuracy of carbon emission data based on the potential risks of flue gas analyzer operation; Analyze the deviation of the static pressure of the gas during the flue gas flow process and evaluate the abnormal degree of the static pressure of the gas during the flue gas flow process, specifically: Obtain static pressure and ambient pressure: static pressure is the pressure of flue gas in the flue, and ambient pressure is the atmospheric pressure of the environment where the flue gas analyzer is located; Calculate the static pressure judgment value: ;in, is the static pressure judgment value, is the static pressure, Environmental pressure; By analyzing the size and density of particles passing through the flue gas analyzer, the potential hazards of particles to carbon emission monitoring are evaluated, specifically: Collect particulate matter samples from flue gas emission sources and obtain particle diameter, particle density and particle concentration; Calculate the comprehensive evaluation index of particle characteristics, the expression is: ;in, It is a comprehensive evaluation index of particle characteristics. is a function of the effect of individual particles on carbon measurement, , is the total number of particles measured The average value of For the The particle diameter of the particles, For the The particle density of particles, For the The particle concentration of particles, They are particle characteristic functions weighting factors for the mean and variance of the resulting values, is the index of the particles, is the total number of particles, is a positive integer, ; The sampling performance of the sampling probe was analyzed to evaluate the stability of the carbon emission monitoring sampling efficiency, specifically: Real-time acquisition of the sampling probe's command extraction flow rate; real-time monitoring of the flue gas flow rate actually extracted by the sampling probe; Monitor multiple actual extracted flue gas flow rates closest to the real time in real time, obtain the command extraction flow rate at the time point corresponding to the actual extracted flue gas flow rate, analyze the degree of deviation between the actual extracted flue gas flow rate and the command extraction flow rate, and calculate the extraction efficiency abnormality index, which is expressed as: ;in, is the extraction efficiency anomaly index, It is the number of the actual extracted flue gas flow rate monitored closest to the real time. is the number of flue gas flow rates actually extracted that is monitored closest to real time, , is a positive integer, For the The actual extracted flue gas flow rate, For the The command extraction flow rate at a time point corresponding to the actual extracted flue gas flow rate.

2. The method for monitoring carbon emissions from thermal power generation according to claim 1, characterized in that: In S2, according to the abnormal degree of gas static pressure during flue gas flow, it is determined whether the external conditions for carbon emission monitoring are normal, specifically: Set a static pressure judgment threshold; when the static pressure judgment value is greater than the static pressure judgment threshold, it is determined that the gas static pressure during the flue gas flow is abnormal; when the static pressure judgment value is less than or equal to the static pressure judgment threshold, it is determined that the gas static pressure during the flue gas flow is normal; When the static pressure of gas during flue gas flow is abnormal, the external conditions for carbon emission monitoring are determined to be abnormal; when the static pressure of gas during flue gas flow is normal, the external conditions for carbon emission monitoring are determined to be normal.

3. The method for monitoring carbon emissions from thermal power generation according to claim 2, characterized in that: When the external conditions of carbon emission monitoring are normal, the potential risk of flue gas flow can be assessed by monitoring the fluctuation of static pressure during flue gas flow: A smoke flow monitoring set is set, which includes multiple static pressure judgment values ​​closest to the real time. The static pressure judgment values ​​of the smoke flow monitoring set are numbered in chronological order. The standard deviation of the static pressure judgment values ​​in the smoke flow monitoring set is calculated by analyzing the volatility of the static pressure judgment values ​​in the smoke flow monitoring set. The expression is: ;in, is the standard deviation of the static pressure judgment value in the smoke flow monitoring set, is the number of static pressure judgment values ​​in the smoke flow monitoring set, It is the number of the static pressure judgment value in the flue gas flow monitoring set. is a positive integer, , For the first smoke flow monitoring set A static pressure judgment value, It is the average value of the static pressure judgment value in the smoke flow monitoring set; The ratio of the standard deviation of the static pressure judgment values ​​in the smoke flow monitoring set to the average value of the static pressure judgment values ​​in the smoke flow monitoring set is marked as the static pressure variation index.

4. The method for monitoring carbon emissions from thermal power generation according to claim 3, characterized in that: In S5, the potential risk level of flue gas fluidity, the potential risk level of particulate matter to carbon emission monitoring, and the stability of sampling efficiency are comprehensively analyzed to evaluate the potential risk level of flue gas analyzer operation, specifically: The static pressure variation index, the comprehensive evaluation index of particulate matter characteristics and the extraction efficiency abnormality index are normalized, and the normalized static pressure variation index, the comprehensive evaluation index of particulate matter characteristics and the extraction efficiency abnormality index are respectively assigned preset proportional coefficients, and the carbon emission monitoring accurate evaluation coefficient is calculated. The degree of operating hidden dangers of the flue gas analyzer is evaluated through the carbon emission monitoring accurate evaluation coefficient; Determine the accuracy of carbon emission data based on the degree of operational risks of the flue gas analyzer: Setting a first threshold for accurate assessment of carbon emission monitoring and a second threshold for accurate assessment of carbon emission monitoring, wherein the first threshold for accurate assessment of carbon emission monitoring is less than the second threshold for accurate assessment of carbon emission monitoring; The carbon emission monitoring accuracy assessment coefficient is compared with the first threshold of carbon emission monitoring accuracy assessment and the second threshold of carbon emission monitoring accuracy assessment, specifically: When the carbon emission monitoring accuracy assessment coefficient is less than the first threshold of the carbon emission monitoring accuracy assessment, it is determined that a carbon emission monitoring accuracy low risk signal is generated; When the carbon emission monitoring accuracy assessment coefficient is greater than or equal to the first threshold of the carbon emission monitoring accuracy assessment, and the carbon emission monitoring accuracy assessment coefficient is less than or equal to the second threshold of the carbon emission monitoring accuracy assessment, it is determined that a carbon emission monitoring accuracy medium risk signal is generated; When the carbon emission monitoring accuracy assessment coefficient is greater than the carbon emission monitoring accuracy assessment second threshold, it is determined that a carbon emission monitoring accuracy high risk signal is generated.

5. A thermal power generation carbon emission monitoring system, used to implement the thermal power generation carbon emission monitoring method according to any one of claims 1 to 4, characterized in that: It includes static pressure anomaly assessment module, external condition judgment module, static pressure fluctuation assessment module, particle hidden danger assessment module, sampling efficiency assessment module and carbon emission credibility assessment module; The static pressure anomaly assessment module analyzes the deviation of the static pressure of the gas during the smoke flow process and assesses the degree of abnormality of the static pressure of the gas during the smoke flow process; The external condition judgment module determines whether the external conditions for carbon emission monitoring are normal based on the abnormal degree of gas static pressure during flue gas flow; When the external conditions of carbon emission monitoring are normal, the static pressure fluctuation assessment module evaluates the hidden dangers of flue gas flow by monitoring the fluctuation of static pressure during flue gas flow; The particle hazard assessment module analyzes the size and density of particles passing through the flue gas analyzer to assess the potential hazard of particles to carbon emission monitoring. The sampling efficiency evaluation module analyzes the sampling performance of the sampling probe and evaluates the stability of the carbon emission monitoring sampling efficiency; The carbon emission credible assessment module conducts a comprehensive analysis of the potential risk level of flue gas fluidity, the potential risk level of particulate matter to carbon emission monitoring, and the stability of sampling efficiency, evaluates the potential risk level of the flue gas analyzer's operation, and determines the accuracy of the carbon emission data based on the potential risk level of the flue gas analyzer's operation.

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