Uncertainty evaluation method suitable for long-period carbon emission continuous monitoring system

Abstract

The invention discloses an uncertainty evaluation method suitable for a long-period carbon emission continuous monitoring system, which combines the actual carbon emission continuous monitoring current situation of each key industry and the demand of a carbon market on long-period carbon emission data of each key industry. The method comprises the following steps: establishing a carbon emission calculation model under practical application to identify the uncertainty sources of the CO2 concentration, the flue gas flow, the flue gas pressure, the flue gas humidity and the flue gas temperature, performing further uncertainty source analysis and quantitative evaluation on the uncertainty sources by modules, and finally synthesizing the total uncertainty to quantify the contribution rate of each source to the total uncertainty. The accuracy and reliability of carbon emission data are efficiently improved, and corresponding improvement and optimization measures are conveniently put forward.

Description

Technical Field

[0001] This invention relates to the technical field of continuous carbon emission monitoring, and in particular to an uncertainty assessment method applicable to long-term continuous carbon emission monitoring systems. Background Technology

[0002] Against the backdrop of the comprehensive implementation of the "dual-carbon" strategy, my country's carbon emission monitoring system is gradually transforming from "extensive statistics" to "precise online monitoring." For key industries that are the main emitters of carbon (such as thermal power, steel, and cement), the accuracy and reliability of their monitoring data directly affect the scientific validity of emission reduction assessments and the credibility of the carbon trading market. Currently, my country mainly uses two technical methods for carbon emission monitoring: accounting methods and monitoring methods. However, both have significant technical limitations, especially in the area of ​​uncertainty assessment, which lacks systematicity and has become a key bottleneck restricting the effective application of carbon emission data.

[0003] The accounting method, as a traditional approach, relies on "activity level data × emission factor" for estimation, but its data traceability is weak and its uncertainty is high. Specifically, activity level data largely depends on ex-post reporting by enterprises, which is subject to lag and subjective bias, and unified collection standards have not yet been established in some stages; at the same time, general emission factors are difficult to accurately reflect the actual production conditions of enterprises, causing the accounting results to deviate from the actual emissions.

[0004] While online monitoring enables real-time data acquisition and has achieved over 60% coverage in key industries, data quality still faces significant challenges due to the complexity of industrial environments, varying performance of monitoring equipment, and outdated uncertainty assessment methods. The current uncertainty assessment system has the following shortcomings: First, the assessment objects are relatively singular, mostly focusing on independent parameters such as CO2 concentration or flow rate, and failing to construct a comprehensive assessment model that coordinates multiple parameters; Second, there is a lack of compatibility with multiple pretreatment processes. Existing evaluation frameworks are often designed for a certain type of cold drying or hot and wet process, and a unified evaluation standard across processes has not yet been established, resulting in low data comparability across production lines and industries. Third, there is a lack of long-term assessment mechanisms. Existing methods mostly focus on short-term (such as hourly) uncertainties and do not include cumulative errors caused by equipment drift, calibration intervals, etc. in the assessment, making it difficult to support emission reduction decisions and carbon trading based on monthly and annual data.

[0005] On the other hand, existing uncertainty assessment methods also face the contradiction of being theoretically rigorous but practically difficult. Some methods (such as Monte Carlo simulation) are mathematically complex and rely on large amounts of data, making them difficult for small and medium-sized enterprises (SMEs) to apply due to a lack of professional expertise and data foundation. On the other hand, some simplified methods (such as estimating based solely on instrument accuracy) often underestimate the true error and fail to objectively reflect the degree of uncertainty in actual monitoring. As a result, the quality of enterprise monitoring data varies greatly, and some data cannot be used for carbon market trading due to excessively high uncertainty. It also increases the complexity and cost of regulatory verification.

[0006] Therefore, with the continuous expansion of my country's carbon market and increasingly stringent emission reduction requirements for key industries, there is an urgent need to construct a set of methods for assessing the uncertainty of continuous carbon emission monitoring that covers multiple industries, is compatible with multiple preprocessing methods, takes into account the needs of long-term monitoring, and is easy to promote and apply. This is to overcome the current predicament of fragmented technology, poor applicability, and difficulty in implementation, and to provide systematic support for improving the quality of carbon emission data. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides an uncertainty assessment method applicable to long-term continuous carbon emission monitoring systems.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An uncertainty assessment method applicable to long-term continuous carbon emission monitoring systems includes the following steps: S1: Based on the principle of continuous emission monitoring systems for flue gas in mainstream industries, a carbon emission calculation model is constructed. The main difference between the cold-drying method and the hot-wet method for flue gas CO2 pretreatment is that the result measured by the cold-drying method is the dry basis concentration, while the result measured by the hot-wet method is the wet basis concentration. S2: Through long-term continuous carbon emission monitoring, the uncertainty input of the carbon emission calculation model is identified, and an overall uncertainty calculation model is constructed accordingly. S3: Divide the identified uncertainty inputs into three modules: flue gas CO2 concentration, flue gas flow rate, and other flue gas parameters, and refine the uncertainty assessment for each module using the overall uncertainty calculation model. S4: Calculate the expanded uncertainty of the overall long-term carbon emissions by combining the carbon emission uncertainty calculation model and the sources of uncertainty of each parameter.

[0009] Specifically, in S1-2, when it is determined that the cold drying method is used for flue gas CO2 pretreatment, the carbon emission calculation model is constructed as follows:

[0010] In the formula, The cumulative carbon dioxide emissions when using the cold drying method, The average concentration of carbon dioxide over the measured long period, For the actual working conditions of wet flue gas flow rate, For flue gas pressure, Moisture content in flue gas For smoke temperature, a constant The standard state temperature of gases as defined by the General Conference on Weights and Measures (CGPM). The pressure value under standard conditions is expressed in Pa. The corresponding overall uncertainty calculation models are divided into absolute uncertainty calculation models and relative uncertainty calculation models. Absolute uncertainty calculation model:

[0011] The formula for calculating the overall relative uncertainty is:

[0012] In the formula, This represents the overall uncertainty obtained when using the cold-drying method; This is the sensitivity coefficient for carbon dioxide concentration; The uncertainty is for the carbon dioxide concentration. This is the flue gas flow sensitivity coefficient; The uncertainty is the flue gas flow rate. This is the flue gas temperature sensitivity coefficient; For the uncertainty of flue gas temperature; This is the flue gas pressure sensitivity coefficient; For the uncertainty of flue gas pressure; This is the humidity sensitivity coefficient for flue gas; The uncertainty is the humidity of the flue gas. This represents the carbon dioxide emission concentration under standard conditions. This refers to the wet flue gas flow rate under actual operating conditions. For smoke temperature; Flue gas pressure, which consists of atmospheric pressure and flue gas static pressure; This refers to the moisture content of the flue gas.

[0013] When the pretreatment method is hot-wet, the carbon emission calculation model is as follows:

[0014] In the formula, Cumulative carbon dioxide emissions when using the hot-wet method; This represents the average concentration of carbon dioxide over the measured long period. This refers to the wet flue gas flow rate under actual operating conditions. For smoke temperature; Flue gas pressure, which consists of atmospheric pressure and flue gas static pressure; The overall uncertainty calculation model is also divided into an absolute uncertainty calculation model and a relative uncertainty calculation model. Absolute uncertainty calculation model:

[0015] The formula for calculating relative uncertainty is:

[0016] in, The total uncertainty obtained when using the hot-wet method, Cumulative carbon dioxide emissions when using the hot-wet method; This represents the carbon dioxide emission concentration under standard conditions. This refers to the wet flue gas flow rate under actual operating conditions. For smoke temperature; It is the flue gas pressure, which consists of atmospheric pressure and flue gas static pressure.

[0017] In S3, the uncertainty sources identified in S2 (flue gas CO2 concentration, flue gas flow rate, flue temperature, flue pressure, and flue humidity) are divided into three modules. The uncertainty assessment modules for flue gas CO2 concentration, flue gas flow rate, and other flue gas parameters are further refined and assessed to achieve a more comprehensive assessment of carbon emission uncertainty and more accurate carbon emission data.

[0018] In S4, the expanded uncertainty of the overall long-term carbon emissions is calculated by substituting the standard uncertainty components obtained from the evaluation of each module into the overall uncertainty calculation model to obtain the combined standard uncertainty. Multiplying this by the coverage factor k yields the expanded uncertainty U. Here, k is typically taken as 2. It is worth noting that the relative uncertainty of each component contributes directly to the total relative uncertainty in the form of a squared term, and its effective sensitivity coefficient can be considered as 1, which greatly facilitates uncertainty assessment. Furthermore, the contribution of each component is quantified according to the uncertainty propagation law. By analyzing the contribution rate of each quantified component to the total uncertainty, the main sources of uncertainty are identified, thereby optimizing and improving the specific implementation methods in the carbon monitoring process, further enhancing the accuracy and reliability of carbon emission monitoring data.

[0019] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention combines the current status of continuous carbon emission monitoring in key industries with the demand of the carbon market for long-term carbon emission data in these industries. By establishing a carbon emission calculation model for practical applications, it identifies the sources of uncertainty in flue gas CO2 concentration, flue gas flow rate, flue gas pressure, flue gas humidity, and flue gas temperature. It further analyzes and quantifies the sources of uncertainty in each module, and finally synthesizes the total uncertainty, quantifies the contribution rate of each source to the total uncertainty, and proposes corresponding improvement and optimization measures, which effectively improves the accuracy and reliability of carbon emission data.

[0020] 2. This invention provides a scientific and effective solution for promoting the formation of more unified assessment standards and norms within the industry, thereby reducing data deviations caused by differences in assessment methods, enabling data to have better consistency across different scales, and achieving accurate and efficient comparative analysis of data for different enterprises in their work such as carbon emission reduction performance assessment and carbon market trading. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the uncertainty assessment method for long-term continuous carbon emission monitoring systems applicable to multiple industries, as described in this invention. Figure 2 The flowchart of the uncertainty assessment of the long-term carbon emission continuous monitoring system in this invention is shown when the cold drying method is used for pretreatment.

[0022] Figure 3 The flowchart of the uncertainty assessment of the long-term carbon emission continuous monitoring system in this invention is shown when the hot-wet method is used for pretreatment. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0024] Example like Figure 1 As shown, this application discloses an uncertainty assessment method applicable to long-term continuous carbon emission monitoring systems, comprising the following steps: S1: A carbon emission calculation model is constructed based on the principles of continuous emission monitoring systems (CEMS) in mainstream industries. Specifically, when a CEMS is in operation, it needs to comprehensively consider the characteristics of the flue gas, the required measurement accuracy, and the operation and maintenance costs to determine whether to use the cold-dry method or the hot-wet method for flue gas pretreatment. Different pretreatment methods correspond to different carbon emission calculation models. When the pretreatment method is cold drying, the carbon emission calculation model is expressed by the following formula:

[0025] In the formula, The cumulative carbon dioxide emissions when using the cold drying method, The average concentration of carbon dioxide over the measured long period, For the actual working conditions of wet flue gas flow rate, For flue gas pressure, Moisture content in flue gas For smoke temperature, a constant The standard state temperature of gases as defined by the General Conference on Weights and Measures (CGPM). The pressure value under standard conditions is expressed in Pa. When the pretreatment method is hot-wet, the carbon emission calculation model is expressed by the following formula:

[0026] In the formula, Cumulative carbon dioxide emissions when using the hot-wet method; This represents the average concentration of carbon dioxide over the measured long period. This refers to the wet flue gas flow rate under actual operating conditions. For smoke temperature; S2: The flue gas pressure is composed of atmospheric pressure and flue gas static pressure. The uncertainty input of the carbon emission calculation model is identified through long-term continuous carbon emission monitoring, and an overall uncertainty calculation model is constructed accordingly.

[0027] Specifically, when the cold-drying method is clearly adopted for flue gas pretreatment, the overall uncertainty calculation model is divided into an absolute uncertainty calculation model and a relative uncertainty calculation model. The absolute uncertainty calculation model should consider the sensitivity coefficients of each input quantity:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] The formula for calculating relative uncertainty is:

[0034] In the formula, This represents the overall uncertainty obtained when using the cold-drying method; This is the sensitivity coefficient for carbon dioxide concentration; The uncertainty is for the carbon dioxide concentration. This is the flue gas flow sensitivity coefficient; The uncertainty is the flue gas flow rate. This is the flue gas temperature sensitivity coefficient; For the uncertainty of flue gas temperature; This is the flue gas pressure sensitivity coefficient; For the uncertainty of flue gas pressure; This is the humidity sensitivity coefficient for flue gas; The uncertainty is the humidity of the flue gas. This represents the carbon dioxide emission concentration under standard conditions. This refers to the wet flue gas flow rate under actual operating conditions. For smoke temperature; Flue gas pressure, which consists of atmospheric pressure and flue gas static pressure; Moisture content of flue gas; When the hot-wet method is clearly adopted for flue gas pretreatment, the overall uncertainty calculation model is divided into an absolute uncertainty calculation model and a relative uncertainty calculation model. The absolute uncertainty calculation model should consider the sensitivity coefficients of each input quantity:

[0035]

[0036]

[0037]

[0038]

[0039] The formula for calculating relative uncertainty is:

[0040] In the formula, This represents the overall uncertainty obtained when using the hot-wet method; Cumulative carbon dioxide emissions when using the hot-wet method; This is the sensitivity coefficient for carbon dioxide concentration; The uncertainty is for the carbon dioxide concentration. This is the flue gas flow sensitivity coefficient; The uncertainty is the flue gas flow rate. This is the flue gas temperature sensitivity coefficient; For the uncertainty of flue gas temperature; This is the flue gas pressure sensitivity coefficient; For the uncertainty of flue gas pressure; This represents the carbon dioxide emission concentration under standard conditions. The flow rate of wet flue gas under actual operating conditions is represented by T; the flue gas temperature is represented by T. It is the flue gas pressure, which consists of atmospheric pressure and flue gas static pressure.

[0041] S3: The identified uncertainty inputs are divided into three modules: flue gas CO2 concentration, flue gas flow rate, and other flue gas parameters. The uncertainty is further refined and evaluated using the overall uncertainty calculation model to achieve a more comprehensive assessment of carbon emission uncertainty and more accurate carbon emission data.

[0042] Uncertainty assessment of flue gas CO2 concentration: The uncertainty assessment of flue gas CO2 concentration adopts the measurement uncertainty method based on long-cycle weekly quality control data, identifying the sources of uncertainty in flue gas CO2 concentration monitoring results as random error and systematic error, as detailed below: The calculation model for assessing the uncertainty of flue gas CO2 concentration is as follows: In the formula, For the uncertainty of carbon dioxide concentration; u U(b) represents the laboratory reproducibility standard uncertainty based on period precision, and u(b) represents the bias standard uncertainty assessed by using certified reference materials of the same matrix.

[0043] Random error originates from laboratory reproducibility, and the standard uncertainty of laboratory reproducibility is... The evaluation involves conducting multiple tests on the concentration measurement equipment using quality control gas over a long period (generally once a week), and quantifying the results through calculation. Specifically, this involves calculating the standard deviation of the weekly quality control standard reading of carbon dioxide concentration from the standard gas concentration. , This represents the difference between the CO2 standard reading and the standard gas concentration measured in week i. Let be the average difference between the CO2 standard reading and the standard gas concentration measured over n weeks. Finally, calculate the relative standard uncertainty. / , This represents the average value of CO2 standard gas used during the quality control period. Systematic errors arise from inter-method and inter-laboratory biases. The standard uncertainty u(b) of the bias is quantified by using certified reference materials with the same matrix, including repeatability of multiple tests on the same standard gas and standard gas uncertainty. Specifically, this involves calculating the relative standard deviation. , Let i be the carbon dioxide concentration measured in the i-th time. The value is the average of the concentrations measured n times; the uncertainty of the standard gas is provided by the calibration certificate of the standard gas used.

[0044] Flue gas flow uncertainty assessment: The principle of flue gas flow measurement is based on the velocity area method, that is, after measuring the velocity at a single point in the flue gas using a velocity measuring device, the velocity is integrated to obtain the cross-sectional average velocity and flow rate. Based on this, a flue gas flow calculation model is constructed, and a corresponding uncertainty calculation model is obtained, as follows: The formula for calculating flue gas flow is:

[0045] The flow rate uncertainty calculation model is also divided into the absolute uncertainty calculation model and the relative uncertainty calculation model. The absolute uncertainty calculation model for flow rate is as follows:

[0046]

[0047]

[0048]

[0049] The model for calculating the relative uncertainty of flow rate is as follows:

[0050] In the formula, Q is the flue gas flow rate; For velocity field coefficients; The average flow velocity measured by the flow velocity monitoring equipment; This refers to the cross-sectional area of ​​the flue. For the uncertainty of flue gas flow rate; For the uncertainty of the velocity field coefficients; The uncertainty of the average flow velocity reading; The uncertainty of the cross-sectional area of ​​the flue; The sensitivity coefficient is the velocity field coefficient. The average flow velocity sensitivity coefficient; This refers to the sensitivity coefficient for the flue cross-sectional area. The uncertainty of the velocity field coefficient is calculated from the standard deviation of the velocity field coefficient. The uncertainty of the average flow velocity indication is taken as the larger of the uncertainty introduced by the repeatability of the flow velocity indication and the uncertainty introduced by the precision of the measuring equipment. The uncertainty of the flue cross-sectional area is calculated based on the flue cross-sectional shape by calculating the relative standard deviation and using the Type B assessment method. Specifically, the relative uncertainty of the velocity field coefficient is based on velocity field coefficient calibration multiple times per day for no less than 3 days, calculated according to the standard deviation of the velocity field coefficient. , The mean value of the velocity field coefficients measured on day i. The average velocity field coefficients measured over n days are given. The relative uncertainty of the average flow velocity indication is determined by comparing the uncertainty introduced by the repeatability of the indication with the uncertainty introduced by the precision of the flow velocity measuring equipment. To avoid overlap between these two uncertainty components, the component with the smaller uncertainty is discarded, and the larger one is ultimately input as the uncertainty of the average flow velocity indication. The repeatability of the indication is calculated using the standard deviation of the flow velocity indication. , Let i be the flue gas velocity measured in the i-th time. The mean flow velocity in the flue is obtained from n measurements. The precision of the measuring equipment is provided by the equipment manufacturer's calibration certificate. The relative uncertainty of the flue cross-sectional area is calculated using a corresponding cross-sectional area uncertainty calculation model based on the actual flue shape. For a circular flue: the cross-sectional area calculation model is as follows: D is the diameter of the flue, and the calculation model for the cross-sectional area uncertainty is as follows: Uncertainty of flue diameter The uncertainty introduced by the repeatability of diameter measurement and the uncertainty introduced by the maximum permissible error of the diameter measuring equipment are combined. The uncertainty introduced by the repeatability of diameter measurement is calculated using the standard deviation of the measured diameter. , Let be the diameter of the flue measured in the i-th time. The mean diameter of the flue is obtained from n measurements. The relative uncertainty introduced by the error of the diameter measuring equipment is calculated according to the Type B uncertainty assessment method. MPE is the maximum permissible error of the measuring device.

[0051] For rectangular flues: the cross-sectional area calculation model is as follows: Let a and b be the side lengths of the rectangular flue, and the cross-sectional area uncertainty calculation model is as follows: Uncertainty of flue side length and The uncertainty introduced by the repeatability of the length measurement and the uncertainty introduced by the maximum permissible error of the length measuring equipment are both combined. The uncertainty introduced by the repeatability of the length measurement is calculated using the standard deviation of the measured length. , Let be the side length of the flue measured in the i-th time. The mean length of the flue gas duct is determined from n measurements. The relative uncertainty introduced by the error of the length measuring equipment is calculated using the Type B uncertainty assessment method. MPE stands for Maximum Permissible Error of Diameter Measuring Equipment.

[0052] Uncertainty assessment for other flue gas parameters: including flue gas temperature, flue gas pressure, and flue gas humidity. Among these, considering that no calibrated standard values ​​are used for comparison during actual monitoring of these parameters, and that experimental calibration data demonstrates that the uncertainty introduced by the measurement repeatability of flue gas temperature, pressure, and humidity is small and can be ignored, therefore the uncertainty of flue gas temperature is... Flue gas pressure uncertainty and flue gas humidity uncertainty All evaluations were conducted using the Class B evaluation method, calculated based on the maximum permissible error (MPE) of the corresponding measuring instrument, as detailed below: Model for calculating the uncertainty of flue gas temperature:

[0053] in, The relative uncertainty of flue gas temperature; This represents the maximum permissible error of the thermometer. This represents the average temperature of the measured flue gas.

[0054] Model for calculating the pressure and uncertainty of flue gas:

[0055]

[0056]

[0057] in, The relative uncertainty of flue gas pressure; The relative uncertainty of atmospheric pressure; The relative uncertainty of the static pressure in the flue gas duct; This represents the maximum permissible error of an atmospheric pressure gauge. This represents the average of the measured atmospheric pressures. This represents the maximum permissible error of the static pressure gauge. This is the average value of the measured static pressure.

[0058] Model for calculating the relative uncertainty of flue gas humidity:

[0059] in, The relative uncertainty of flue gas humidity; This represents the maximum permissible error of the hygrometer. This represents the average moisture content measured.

[0060] S4: The expanded uncertainty of the overall long-term carbon emissions is calculated by combining the carbon emission uncertainty calculation model and the sources of uncertainty for each parameter. The combined calculation of the expanded uncertainty of the overall long-term carbon emissions is based on the uncertainty propagation law, which involves substituting the standard uncertainty components obtained from the evaluation of each module into the overall uncertainty calculation model to obtain the combined standard uncertainty. Multiplying this by the coverage factor k yields the expanded uncertainty U. , where k is 2. It should be noted that the relative uncertainty of each component contributes directly to the total relative uncertainty in the form of a squared term, and its effective sensitivity coefficient can be regarded as 1, which greatly facilitates the uncertainty assessment work.

[0061] Furthermore, the contribution of each component is quantified according to the uncertainty propagation law. By analyzing the contribution rate of each quantified component to the total uncertainty, the main sources of uncertainty are identified, thereby optimizing and improving the specific implementation methods in the carbon monitoring process and further enhancing the accuracy and reliability of carbon emission monitoring data.

[0062] Application examples When using the cold-drying method for preprocessing in a long-term continuous carbon emission monitoring system for carbon emission monitoring in a certain steel industry, the steps for uncertainty assessment are as follows: Step 1: The collected carbon emission data from a steel industry was preprocessed using the cold-drying method, and then calculated based on the established long-term carbon emission calculation model using the cold-drying method.

[0063] Among them, G represents the cumulative carbon dioxide emissions when the cold drying method is used; This represents the carbon dioxide emission concentration under standard conditions. This refers to the wet flue gas flow rate under actual operating conditions. is the flue gas temperature; P is the flue gas pressure, which consists of atmospheric pressure and flue gas static pressure. This refers to the moisture content in the flue gas.

[0064] Step 2: Through long-term continuous monitoring, identify the sources of uncertainty and sensitivity coefficients of the carbon emission calculation model, and construct an overall uncertainty calculation model accordingly.

[0065] In this embodiment of the invention, the formula for calculating the uncertainty of long-term carbon emissions using the cold-drying method is as follows:

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] In the formula, This represents the overall uncertainty obtained when using the hot-wet method; Cumulative carbon dioxide emissions when using the hot-wet method; This is the sensitivity coefficient for carbon dioxide concentration; The uncertainty is for the carbon dioxide concentration. This is the flue gas flow sensitivity coefficient; The uncertainty is the flue gas flow rate. This is the flue gas temperature sensitivity coefficient; For the uncertainty of flue gas temperature; This is the flue gas pressure sensitivity coefficient; For the uncertainty of flue gas pressure; This represents the carbon dioxide emission concentration under standard conditions. The flow rate of wet flue gas under actual operating conditions is represented by T; the flue gas temperature is represented by T. It is the flue gas pressure, which consists of atmospheric pressure and flue gas static pressure.

[0072] Step 3: Divide the sources of uncertainty into three modules: flue gas CO2 concentration, flue gas flow rate, and other flue gas parameters, and conduct a detailed evaluation of the uncertainty for each module. In this application example, the flue gas CO2 concentration relative uncertainty assessment module measures the concentration of the same standard gas eight times as a repeatability test, as shown in Table 1; and continuously measures the standard concentration for 52 weeks throughout the year, as shown in Table 2. The standard gas concentration used is 40%, and the standard gas uncertainty is 1% as provided by its calibration certificate.

[0073] Table 1 Repeatability Test Data

[0074] Table 2 Annual GMP Concentration Data

[0075] Flue gas CO2 concentration relative uncertainty assessment module: In the uncertainty assessment method for the long-cycle multi-industry continuous carbon emission monitoring system provided in the embodiments of the present invention, the uncertainty assessment of flue gas CO2 concentration adopts the measurement uncertainty method based on long-cycle weekly quality control data, and identifies the sources of uncertainty of flue gas CO2 concentration monitoring results as random error and systematic error.

[0076] Construct a model for calculating the relative uncertainty of flue gas CO2 concentration:

[0077] Random error originates from laboratory reproducibility, and the standard uncertainty of laboratory reproducibility is... The evaluation involves conducting multiple tests on the concentration measurement equipment using quality control gas over a long period (generally once a week), and quantifying the results through calculation. Specifically, this involves calculating the standard deviation of the weekly quality control standard reading of carbon dioxide concentration from the standard gas concentration. , This represents the difference between the CO2 standard reading and the standard gas concentration measured in week i. Let be the average difference between the CO2 standard reading and the standard gas concentration measured over n weeks. Finally, calculate the relative standard uncertainty. / , This represents the average value of CO2 standard gas used during the quality control period. Systematic errors arise from inter-method and inter-laboratory biases. The standard uncertainty u(b) of the bias is quantified by using certified reference materials with the same matrix, including repeatability of multiple tests on the same standard gas and standard gas uncertainty. Specifically, this involves calculating the relative standard deviation. , Let i be the carbon dioxide concentration measured in the i-th time. The value is the average of the concentrations measured n times; the uncertainty of the standard gas is provided by the calibration certificate of the standard gas used.

[0078] Based on the above calculation method and substituting the measured data, the specific calculation results of the flue gas CO2 concentration relative uncertainty assessment module are shown in the table below:

[0079] In this embodiment of the invention, the flue gas flow uncertainty calculation module continuously measured and calibrated for 3 days, with 5 sets of velocity field coefficients each day; measured the weekly average point flow velocity over 52 weeks of a year; and measured 6 sets of diameters for the circular flue. As shown in the table below, Table 3 Calibration data for velocity field coefficients

[0080] Table 4. Diameter data for circular flues

[0081] The formula for constructing the flue gas flow calculation model is as follows:

[0082] The formula for calculating the relative uncertainty of flue gas flow rate is as follows:

[0083] Where Q is the flue gas flow rate; For velocity field coefficients; The average flow velocity measured by the flow velocity monitoring equipment; The relative uncertainty of flue gas flow rate; The relative uncertainty of the velocity field coefficients; The relative uncertainty of the average flow velocity reading; This represents the relative uncertainty of the cross-sectional area of ​​the flue.

[0084] The relative uncertainty of the velocity field coefficients is calculated based on the standard deviation of the velocity field coefficients, using a calibration process that lasts for at least three days and is performed multiple times daily. , The mean value of the velocity field coefficients measured on day i. The average velocity field coefficients measured over n days; The relative uncertainty of the average flow velocity indication is calculated by comparing the uncertainty introduced by the repeatability of the indication with the uncertainty introduced by the precision of the flow velocity measuring device. To avoid overlap between these two uncertainty components, the larger one is used as the uncertainty of the average flow velocity indication. The repeatability of the indication is calculated using the standard deviation of the flow velocity indication. , Let i be the flue gas velocity measured in the i-th time. The mean flue gas velocity is the result of n measurements. The precision of the measuring equipment is provided by the calibration certificate issued by the equipment manufacturer. The relative uncertainty of the flue cross-sectional area is calculated using a corresponding cross-sectional area uncertainty calculation model based on the actual flue shape. For a circular flue, the cross-sectional area calculation model is as follows: D is the diameter of the flue, and the calculation model for the cross-sectional area uncertainty is as follows: Uncertainty of flue diameter The uncertainty introduced by the repeatability of diameter measurement and the uncertainty introduced by the maximum permissible error of the diameter measuring equipment are combined. The uncertainty introduced by the repeatability of diameter measurement is calculated using the standard deviation of the measured diameter. , Let be the diameter of the flue measured in the i-th time. The mean diameter of the flue is obtained from n measurements. The relative uncertainty introduced by the error of the diameter measuring equipment is calculated according to the Type B uncertainty assessment method. MPE is the maximum permissible error of the measuring device.

[0085] Based on the above calculation method and substituting the measured data, the specific calculation results of the flue gas flow relative uncertainty assessment module are shown in the table below:

[0086] In this embodiment of the invention, the other flue gas parameter relative uncertainty assessment module measures 6 sets of flue gas temperature data, flue gas static pressure data and flue gas humidity data respectively. The maximum permissible error of the static pressure gauge is ±0.1 kPa, the maximum permissible error of the atmospheric pressure gauge is 0.50%, the maximum permissible error of the thermometer is ±3℃, and the maximum permissible error of the hygrometer is 15%.

[0087] Model for calculating the relative uncertainty of flue gas temperature:

[0088] in, The relative uncertainty of flue gas temperature; This represents the maximum permissible error of the thermometer. This represents the average temperature of the measured flue gas.

[0089] Model for calculating the relative uncertainty of flue gas pressure:

[0090]

[0091]

[0092] in, The relative uncertainty of flue gas pressure; The relative uncertainty of atmospheric pressure; The relative uncertainty of the static pressure in the flue gas duct; This represents the maximum permissible error of an atmospheric pressure gauge. This represents the average of the measured atmospheric pressures. This represents the maximum permissible error of the static pressure gauge. This is the average value of the measured static pressure.

[0093] Model for calculating the relative uncertainty of flue gas humidity:

[0094] in, The relative uncertainty of flue gas humidity; This represents the maximum permissible error of the hygrometer. This represents the average moisture content measured.

[0095] Based on the above calculation method and substituting the measured data, the specific calculation results of the relative uncertainty assessment module for other flue gas parameters are shown in the table below:

[0096] Step four involves calculating the expanded uncertainty of the overall long-term carbon emissions, analyzing the contribution rate of each uncertainty component, and proposing suggestions for optimizing the monitoring system based on this analysis.

[0097] In this embodiment of the invention, the relative uncertainty of each component is directly contributed to the total relative uncertainty in the form of a squared term, and its effective sensitivity coefficient can be regarded as 1. After calculating the relative uncertainty of each component through the above method steps, the components are synthesized according to the following formula: 3.70% The expanded uncertainty is: U(G) = 3.70% × 2 = 7.40% (k = 2) Based on the quantified relative uncertainty of carbon emissions described above, and according to the law of uncertainty propagation, the contribution rate of each component to the total uncertainty is further calculated, as shown in the table below:

[0098] Further contribution analysis was performed on the relative uncertainty of flue gas flow, which has the largest contribution rate to the uncertainty of total carbon emissions. The details are shown in the table below:

[0099] In this application example, through the above analysis of the contribution rate of uncertainty of each component, it can be clearly concluded that the measurement of flue gas flow is the key to affecting the uncertainty of carbon emissions. Among them, the accuracy of the flow velocity measuring equipment has the most significant impact on the uncertainty of flue gas flow measurement. Therefore, in order to reduce the uncertainty of the long-term continuous carbon emission monitoring system, it is recommended to use higher precision flow velocity measuring equipment and improve the calibration accuracy of the velocity field coefficient.

[0100] Since the evaluation process of the cold-dry method for flue gas pretreatment already includes the evaluation process of the hot-wet method, the only difference between the two is that the hot-wet method lacks a humidity uncertainty evaluation module compared to the cold-dry method. Therefore, there is no need to describe the evaluation process of the hot-wet method separately.

[0101] Of course, the present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and modifications according to the present invention, but these corresponding changes and modifications should all fall within the protection scope of the appended claims.

Claims

1. A method for uncertainty evaluation suitable for long-term carbon emission continuous monitoring system, characterized in that, The method comprises the following steps: S1: constructing a carbon emission calculation model according to the principle of a mainstream industrial flue gas emission continuous monitoring system; S2: identifying uncertain input of the carbon emission calculation model through long-period continuous carbon emission monitoring, and constructing an overall uncertainty calculation model according to the same; S3: dividing the identified uncertain input into three modules of flue gas CO2 concentration, flue gas flow and other flue gas parameters, and respectively performing uncertainty refinement evaluation through the overall uncertainty calculation model; S4: synthesizing and calculating the overall long-period carbon emission expansion uncertainty through the carbon emission uncertainty calculation model and the parameter uncertainty sources.

2. The method for uncertainty evaluation applicable to long-term carbon emission continuous monitoring system according to claim 1, characterized in that, In S1, when the flue gas emission continuous monitoring system selects a cold dry method to pretreat the flue gas, the carbon emission calculation model constructed is: ; In the formula, is the cumulative carbon dioxide emissions when using the cold drying method, is the average concentration of carbon dioxide in the measured long period, is the actual working condition of the wet flue gas flow, is the flue gas pressure, is the moisture content in the flue gas, is the flue gas temperature, constant is the gas standard state temperature stipulated by the International Measurement Conference (CGPM), is the pressure value under standard conditions, unit pa.

3. The method for uncertainty evaluation applicable to long-term carbon emission continuous monitoring system according to claim 2, characterized in that, The corresponding overall uncertainty calculation model is divided into an absolute uncertainty calculation model and a relative uncertainty calculation model, the absolute uncertainty calculation model is: ; The relative uncertainty calculation model formula is: ; wherein is the overall uncertainty obtained when using the cold drying method; is the sensitivity coefficient for carbon dioxide concentration; is the uncertainty for carbon dioxide concentration; is the sensitivity coefficient for flue gas flow; is the uncertainty for flue gas flow; is the sensitivity coefficient for flue gas temperature; is the uncertainty for flue gas temperature; is the sensitivity coefficient for flue gas pressure; is the uncertainty for flue gas pressure; is the sensitivity coefficient for flue gas humidity; is the uncertainty for flue gas humidity; is the carbon dioxide emission concentration at standard conditions; is the wet flue gas flow under actual conditions; is the flue gas temperature; is the flue gas pressure, consisting of atmospheric pressure and flue gas static pressure; is the flue gas moisture content.

4. The method for uncertainty evaluation suitable for long-term carbon emission continuous monitoring system according to claim 1, characterized in that, In S1, when the flue gas emission continuous monitoring system selects a hot and wet method to pretreat the flue gas, the carbon emission calculation model constructed is: ; In the formula, Cumulative carbon dioxide emissions when using the hot and wet method; The average concentration of carbon dioxide in the measured long period; The wet flue gas flow under actual working conditions; The flue gas temperature; The flue gas pressure, composed of atmospheric pressure and flue gas static pressure.

5. The uncertainty evaluation method suitable for long-term carbon emission continuous monitoring system according to claim 4, characterized in that, The corresponding overall uncertainty calculation model is also divided into an absolute uncertainty calculation model and a relative uncertainty calculation model, the uncertainty calculation model formula is: ; The relative uncertainty calculation model formula is: ; wherein is the overall uncertainty obtained when using the hot-wet method; is the cumulative carbon dioxide emission obtained when using the hot-wet method; is the carbon dioxide concentration sensitivity coefficient; is the carbon dioxide concentration uncertainty; is the flue gas flow sensitivity coefficient; is the flue gas flow uncertainty; is the flue gas temperature sensitivity coefficient; is the flue gas temperature uncertainty; is the flue gas pressure sensitivity coefficient; is the flue gas pressure uncertainty; is the carbon dioxide emission concentration at standard conditions; is the wet flue gas flow under actual conditions; T is the flue gas temperature; P is the flue gas pressure, consisting of atmospheric pressure and flue gas static pressure.

6. The method for uncertainty evaluation applicable to long-term carbon emission continuous monitoring system according to any one of claims 3 and 5, characterized in that, In S3, the flue gas CO2 concentration uncertainty evaluation module, the uncertainty calculation model is: wherein, is the carbon dioxide concentration uncertainty; u is the within-laboratory reproducibility standard uncertainty based on the period precision, and u(b) is the bias standard uncertainty evaluated by using a certified reference material of the same matrix.

7. The method for uncertainty evaluation applicable to long-term carbon emission continuous monitoring system according to claim 6, characterized in that, In S3, in the flue gas flow uncertainty assessment module, the flue gas flow calculation model is: ; The flow uncertainty calculation model is also divided into an absolute uncertainty calculation model and a relative uncertainty calculation model, the flow absolute uncertainty calculation model is: ; The flow relative uncertainty calculation model is: ; where Q is the flue gas flow rate; is the velocity field coefficient; is the average flow rate measured by the flow rate monitoring device; is the flue cross-sectional area; is the flue flow rate uncertainty; is the velocity field coefficient uncertainty; is the average flow rate indication uncertainty; is the flue cross-sectional area uncertainty; is the sensitivity coefficient of the velocity field coefficient; is the sensitivity coefficient of the average flow rate; is the sensitivity coefficient of the flue cross-sectional area; wherein the velocity field coefficient uncertainty is calculated from the standard deviation of the velocity field coefficient, the average flow rate indication uncertainty is the greater of the uncertainty introduced by the repeatability of the flow rate indication and the uncertainty introduced by the precision of the measuring device, and the flue cross-sectional area uncertainty is calculated from the relative standard deviation and the B evaluation method according to the shape of the flue cross-sectional area.

8. The uncertainty evaluation method suitable for long-term carbon emission continuous monitoring system according to claim 7, characterized in that, In S3, the other flue gas parameter uncertainty assessment module includes flue gas temperature, flue gas pressure and flue gas humidity; wherein, the flue gas temperature uncertainty , the flue gas pressure uncertainty and the flue gas humidity uncertainty all adopt the B type assessment method, and are calculated according to the maximum permissible error (MPE) of the corresponding measuring instrument.

9. The uncertainty evaluation method suitable for long-term carbon emission continuous monitoring system according to claim 8, characterized in that, In S4, the synthesis calculation of the extended uncertainty of the total long-period carbon emission is according to the uncertainty propagation law, and the standard uncertainty components obtained by evaluating each module are substituted into the total uncertainty calculation model to obtain the synthesis standard uncertainty , and multiplied by the containing factor k to obtain the extended uncertainty U , where k is usually 2.

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