CN118130729A - CEMS-based carbon emission monitoring system and method - Google Patents
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 141
- 238000000034 method Methods 0.000 title claims abstract description 102
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 73
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 73
- 238000000738 capillary electrophoresis-mass spectrometry Methods 0.000 title claims abstract 14
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims abstract description 220
- 229910002092 carbon dioxide Inorganic materials 0.000 claims abstract description 110
- 239000001569 carbon dioxide Substances 0.000 claims abstract description 110
- 238000012937 correction Methods 0.000 claims abstract description 93
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims abstract description 43
- 239000003546 flue gas Substances 0.000 claims abstract description 43
- 230000008569 process Effects 0.000 claims abstract description 43
- 238000012545 processing Methods 0.000 claims abstract description 39
- 239000000779 smoke Substances 0.000 claims abstract description 35
- 230000004927 fusion Effects 0.000 claims abstract description 31
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- 238000005070 sampling Methods 0.000 claims abstract description 17
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- 239000007789 gas Substances 0.000 claims description 43
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Abstract
The invention relates to the technical field of carbon emission monitoring, in particular to a CEMS-based carbon emission monitoring system and a CEMS-based carbon emission monitoring method. The sampling unit is responsible for extracting a smoke sample from a flue; the gaseous pollutant monitoring unit monitors the concentration of carbon dioxide in the flue gas in real time; the smoke emission parameter monitoring unit continuously monitors and records key physical parameters of smoke emission; the data processing unit receives and processes the data monitored by the gaseous pollutant monitoring unit and the smoke emission parameter monitoring unit; the data processing unit comprises a data correction module, a data fusion module and a data processing module; the data processing module optimizes the carbon emission statistical algorithm based on the carbon emission statistical algorithm by introducing the sectional integration, and reduces the time window of analysis by the sectional integration, so that the estimation of the flow and the concentration is closer to the actual instantaneous value, the accuracy of the emission estimation is improved, and the problem of emission estimation deviation caused by neglecting the instantaneous fluctuation in the traditional method is solved.
Description
Technical Field
The invention relates to the technical field of carbon emission monitoring, in particular to a CEMS-based carbon emission monitoring system and a CEMS-based carbon emission monitoring method.
Background
CEMS-based carbon emission monitoring system for continuously monitoring greenhouse gases, particularly carbon dioxide, emitted into the atmosphere from stationary sources of pollution (e.g., coal-fired power plants, industrial boilers, chemical plants, etc.), particularlyConcentration and total emissions.
In the traditional carbon emission monitoring system, the instantaneous fluctuation of the flow and the concentration in the emission process is ignored by calculating the product of the average concentration and the flow in a fixed time period, so that the deviation of carbon dioxide emission calculation is larger, and under the scene of rapid change of the emission rate, accurate emission assessment is difficult to provide, and the timeliness and the effectiveness of an emission control strategy are influenced; accordingly, CEMS-based carbon emission monitoring systems and methods are provided.
Disclosure of Invention
The invention aims to provide a carbon emission monitoring system and a carbon emission monitoring method based on CEMS (cell emission management system), which are used for solving the problems that the traditional carbon emission monitoring system provided in the background art ignores instantaneous fluctuation of flow and concentration in the emission process by calculating the product of average concentration and flow in a fixed time period, so that the calculated deviation of carbon dioxide emission is larger, accurate emission assessment is difficult to provide under the scene of rapid change of the emission rate, and the timeliness and effectiveness of an emission control strategy are influenced.
To achieve the above object, in one aspect, the present invention is directed to a CEMS-based carbon emission monitoring system, comprising:
The sampling unit is responsible for extracting a flue gas sample from the flue and is used for providing data support for subsequent pollutant concentration analysis, emission parameter measurement and final carbon emission calculation;
the gaseous pollutant monitoring unit is used for monitoring the concentration of carbon dioxide in the flue gas in real time and providing data support for calculating the carbon dioxide emission;
The flue gas emission parameter monitoring unit is used for continuously monitoring and recording key physical parameters of flue gas emission, wherein the key physical parameters comprise flow rate, temperature, pressure and humidity parameters of the flue gas, and necessary input data are provided for accurate calculation of carbon emission;
The data processing unit is used for receiving and processing the data monitored by the gaseous pollutant monitoring unit and the smoke emission parameter monitoring unit and calculating the actual carbon dioxide emission;
The data processing unit comprises a data correction module, a data fusion module and a data processing module; correcting the concentration of carbon dioxide in the monitored flue gas through a data correction module;
the data processing module optimizes the carbon emission statistical algorithm based on the carbon emission statistical algorithm by introducing a sectional integral, and is used for ensuring the accuracy of carbon dioxide emission measurement;
The early warning unit compares the monitored data with the preset emission standard in real time according to the preset emission standard and alarms the abnormal data.
As a further improvement of the technical scheme, the gaseous pollutant monitoring unit adopts a non-dispersive infrared analyzer to analyze the smoke sample in the sampling unit and distinguish the carbon dioxide absorption spectrum from the spectrum of other gases, thereby completing the concentration measurement of carbon dioxide.
As a further improvement of the technical scheme, the smoke emission parameter monitoring unit comprises a flow rate monitoring module, a temperature monitoring module, a pressure monitoring module and a humidity monitoring module;
the flow speed monitoring module is used for monitoring the flow speed of the flue gas in the discharging process in real time; specifically, the change of the flow velocity of the flue gas is accurately captured through the pitot tube equipment;
The temperature monitoring module is used for monitoring the temperature of the flue gas in real time, evaluating the emission characteristics and the control efficiency of pollutants, and providing data support for correcting other monitoring parameters (flow rate and gas concentration);
The pressure monitoring module is used for monitoring the pressure state in the flue in real time, including pipeline resistance, fan work efficiency and the like. The pressure monitoring is helpful for diagnosing whether the system is blocked or leaked, and is also a basis for calculating the flow rate and adjusting the working state of the sampling system;
The humidity monitoring module is used for measuring the humidity content in the flue gas in real time, and generally adopts a high-temperature capacitance humidity sensor to measure and provide data support for calculating the emission of the wet base converted into the dry base.
As a further improvement of the technical scheme, the data correction module comprises a temperature and humidity correction module, a flow velocity correction module and a cross sensitivity correction module;
the temperature and humidity correction module corrects the measured gas concentration by adopting a dry basis correction algorithm according to the monitored flue gas temperature and humidity data to obtain dry basis concentration, wherein the dry basis correction algorithm comprises the following specific expression:
In the method, in the process of the invention, The dry basis concentration is represented, namely the actual gas concentration after the influence of moisture is subtracted; /(I)Representing a directly measured concentration of a gas containing the effects of moisture; /(I)Represents the humidity ratio, i.e. the ratio of the partial pressure of water vapor to the total air pressure;
The cross sensitivity correction module removes interference of other gas components on a carbon dioxide measurement result based on a cross sensitivity correction algorithm, introduces error feedback to adjust a correction coefficient, realizes iterative optimization, ensures the purity and accuracy of data, and relates to a specific expression as follows:
In the method, in the process of the invention, Representing corrected true carbon dioxide concentration; /(I)Representing the concentration of carbon dioxide on a dry basis, i.e., uncorrected, measured directly by the sensor; /(I)Representing other gases/>Is a concentration of (2); /(I)Representing gas/>Cross-sensitivity correction coefficients for carbon dioxide measurements (determined by laboratory calibration procedures); /(I)Index variables representing each gas except carbon dioxide;
the flow rate correction module corrects the actual measured flow rate by utilizing a gas state equation according to the actual monitoring temperature and pressure conditions of the flue gas and converts the actual measured flow rate into the flow rate under the standard condition, thereby eliminating measurement errors caused by working condition fluctuation and correcting the actual flow rate to the flow rate under the standard condition The specific correction process comprises the following steps:
；
In the method, in the process of the invention, Representing the carbon dioxide flow rate under standard conditions; /(I)Representing the actual measured carbon dioxide flow rate, i.e. the monitored uncorrected flue gas flow rate; /(I)Representing the actual measured flue gas temperature; /(I)The standard temperature is represented, and the value is 273.15K, namely 0 ℃; /(I)Representing the actual measured flue gas pressure; /(I)The standard atmospheric pressure was represented, and the value was 101.325kPa.
As a further improvement of the technical scheme, the specific steps for adjusting the cross sensitivity correction coefficient through the error feedback are as follows:
S1, specific expression based on cross sensitivity correction, for directly measuring carbon dioxide dry basis concentration Performing primary correction to obtain corrected carbon dioxide concentration/>；
In the superscriptRepresenting the result of the initial correction;
s2, after the primary correction, the sensor continuously monitors the actual emission condition in real time, including the uncorrected carbon dioxide concentration Concentration of other gases/>And corrected concentration/>；
S3, defining errorsTrue carbon dioxide concentration/>, measured for corrected concentration and higher accuracyThe difference between:
；
In the method, in the process of the invention, Representing the error; /(I)Representation relative to corrected concentration/>Higher accuracy carbon dioxide concentration of (a);
S4, according to the error Fine tuning correction coefficient/>, by iterative algorithmFor each gas/>The update of the correction coefficient involves the following expression:
；
In the method, in the process of the invention, Represents the/>Gas/>, at the time of iterationA cross sensitivity correction coefficient for carbon dioxide measurement; Represents the/> Gas/>, at the time of iterationA cross sensitivity correction coefficient for carbon dioxide measurement; /(I)Representing the iteration number; /(I)Representing a learning rate for controlling the magnitude of correction coefficient adjustment; /(I)Representing the partial derivative of the error with respect to the correction coefficient, representing the direct effect of the correction coefficient on the error; /(I)Index, each of the interfering gases other than carbon dioxide;
S5, using the updated correction coefficient And (5) correcting again:
；
In the method, in the process of the invention, Expressed in/>Correction concentration after the second iteration;
s6, repeating the steps S3 to S5 until the error is corrected Meeting a predetermined accuracy criterion or reaching a maximum number of iterations.
As a further improvement of the technical scheme, the data fusion module is used for integrating multi-source data from different sensors and carrying out data fusion based on a data fusion algorithm, and is used for reducing errors of single-point measurement and accidental errors, so that the reliability and accuracy of overall data are improved, and the data fusion algorithm specifically comprises:
；
In the method, in the process of the invention, Representing the dry basis concentration of the carbon dioxide after data fusion; /(I)Representing the passage of/>Second iteration/>The dry basis concentration of carbon dioxide for each sensor; /(I)Represents the/>Weights of the individual sensors; /(I)Representing the total number of sensors; /(I)Representing an index variable;
Wherein, the current credibility of the sensor is measured by introducing performance index, and the weight is dynamically adjusted according to the current credibility ：
；
In the method, in the process of the invention,Expressed in time/>(1 /)Dynamic weights of the individual sensors; /(I)Represents the/>The individual sensors are in timeIs determined by the estimation error variance of (a); /(I)Representing a small constant for avoiding the problem of division by zero;
The weight of each sensor in the fusion process is dynamically adjusted according to the error variance of each sensor at different time points, the sensor with small error variance (namely good performance index) can obtain higher weight, so that the measured data of the sensor can be endowed with more trust and influence when fused, otherwise, the weight is lower, and the dynamic adjustment mechanism is beneficial to improving the accuracy and the robustness of the whole data fusion;
By integrating data from different monitoring subsystems, including gaseous pollutant concentration, flue gas emission parameters and the like, the data from multiple sources can be effectively fused, and a complete and consistent information basis is provided for subsequent emission calculation.
As a further improvement of the technical scheme, the carbon emission statistical algorithm adopts a flow-concentration method and combines a time sequence to calculate the accumulated emission mass of carbon dioxide in a certain period of time, and the specific expression related to the carbon emission statistical algorithm is as follows:
；
In the method, in the process of the invention, Represents carbon dioxide over a period of time/>To/>Accumulated emission mass in; /(I)Expressed in time/>Carbon dioxide flow rate under standard conditions; /(I)Expressed in time/>The dry basis concentration of the carbon dioxide after data fusion; Represents the molar mass of carbon dioxide, equal to 44.01g/mol, for converting the concentration units into mass flow units; Representing time bins, representing minute time intervals within the integration interval.
As a further improvement of the technical scheme, the carbon emission statistical algorithm is optimized by introducing sectional integration, and the time period is shortenedSegmentation into/>Inter-cell, the time length between each cell is/>Wherein/>Represents the/>Between cells, an；
The statistical arithmetic expression of the carbon emission after the segmentation optimization is as follows:
；
In the method, in the process of the invention, Represents the/>An average of flow rates among the cells; /(I)Represents the/>Average value of concentration among cells; /(I)Represents carbon dioxide over a period of time/>The accumulated emission quality in the cells, namely the sum of the emission quantity among the cells; /(I)Representing segmentation period/>Representing the number of segments into which the data is subdivided for segment integration calculation; /(I)Represents an index variable representing a segmentation period/>Obtained/>Each cell;
The introduction of the segmented integration method is an efficient and practical strategy for processing continuous time series data, especially for large-scale carbon emission monitoring and management, by improving the calculation efficiency, enhancing the flexibility and accuracy of data processing and supporting real-time monitoring.
As a further improvement of the technical scheme, the early warning unit comprises a threshold setting module, a real-time comparison module and an alarm triggering module;
The threshold setting module is used for setting an upper limit of carbon dioxide concentration and an upper limit of carbon dioxide emission;
the real-time comparison module is used for comparing the monitored data with a preset emission standard in real time, identifying emission data exceeding a threshold value, and judging whether the carbon emission has potential illegal emission risk or not;
The alarm triggering module is based on the analysis result of the real-time comparison module, once the data analysis result shows that the emission data exceeds the threshold value, the module immediately starts an alarm mechanism, and the alarm mechanism comprises sound alarm, visual alarm (such as flashing light), short message or email and informs a preset operator or manager to ensure that the information is rapidly conveyed.
In another aspect, the present invention provides a CEMS-based carbon emission monitoring method for use in a CEMS-based carbon emission monitoring system as described in any one of the above, comprising the steps of:
s10.1, firstly, extracting a smoke sample from a flue through a sampling unit, and monitoring and recording parameters of smoke flow rate, temperature, pressure and humidity in the smoke sample through a smoke emission parameter monitoring unit;
S10.2, simultaneously, analyzing a smoke sample by a gaseous pollutant monitoring unit through a non-dispersive infrared analyzer, distinguishing a carbon dioxide absorption spectrum from spectrums of other gases, and realizing concentration measurement of carbon dioxide;
S10.3, receiving and processing data monitored by the gaseous pollutant monitoring unit and the smoke emission parameter monitoring unit through the data processing unit, wherein the original data are adjusted through the data correction module so as to eliminate the influence of environmental factors and equipment cross and ensure the purity and the accuracy of a measurement result;
The data fusion module integrates multi-source data, introduces performance indexes to dynamically adjust sensor weights, selectively depends on high-precision data sources based on the performance indexes, and improves the reliability of final emission calculation;
the data processing module is used for combining a flow-concentration method with time sequence analysis, optimizing by a sectional integration method, and calculating the total carbon dioxide emission in real time, so that the efficiency of processing a large amount of data is improved;
And S10.4, finally, comparing the real-time data with a preset threshold value through an early warning unit, and monitoring whether the emission behavior is compliant or not, and starting an alarm mechanism through an alarm triggering module by the early warning unit once the real-time carbon emission data is detected to exceed the preset threshold value, so that timely taking corrective measures is ensured, and the occurrence of illegal emission events is effectively prevented.
Compared with the prior art, the invention has the beneficial effects that:
1. In the CEMS-based carbon emission monitoring system and method, the multi-source data from different sensors are integrated, and data fusion is carried out based on a data fusion algorithm, so that single-point measurement errors are reduced, accidental errors are reduced, and the reliability and accuracy of the whole data are improved;
Meanwhile, the current credibility of the sensor is measured by introducing performance indexes, and the sensor weight involved in the data fusion process is dynamically adjusted accordingly, wherein the sensor with small error variance (namely good performance indexes) can obtain higher weight, so that the measured data of the sensor are endowed with more trust and influence when fused, otherwise, the weight is lower, and the dynamic adjustment mechanism is beneficial to improving the accuracy and the robustness of the whole data fusion.
2. In the CEMS-based carbon emission monitoring system and method, a data processing module calculates the accumulated emission quality of carbon dioxide in a certain period of time based on a carbon emission statistical algorithm, and optimizes the carbon emission statistical algorithm by introducing a sectional integral, and the flow and the concentration in each time window are independently calculated and summarized through the time window of the sectional integral reduction analysis, so that the dynamic change in the emission process can be finely tracked; and by reducing the time window of analysis, the sectional integration method enables the estimation of the flow and the concentration to be closer to the actual instantaneous value, thereby improving the accuracy of emission estimation and solving the problem of emission estimation deviation caused by neglecting instantaneous fluctuation in the traditional method.
Drawings
Fig. 1 is an overall flow diagram of the present invention.
The meaning of each reference sign in the figure is:
1. a sampling unit; 2. a gaseous contaminant monitoring unit;
3. A smoke emission parameter monitoring unit;
4. A data processing unit; 41. a data correction module; 42. a data fusion module; 43. a data processing module;
5. And an early warning unit.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
Referring to fig. 1, a CEMS-based carbon emission monitoring system is provided, which includes a sampling unit 1, wherein the sampling unit 1 is responsible for extracting a flue gas sample from a flue, and is used for providing data support for subsequent pollutant concentration analysis, emission parameter measurement and final carbon emission calculation, so as to ensure that the sample can accurately reflect the actual emission condition; the sampling unit 1 comprises a sampling probe, a sampling pipeline and necessary pretreatment devices (such as a filter, a dehumidifier and the like), wherein the pretreatment devices are used for removing impurities and protecting subsequent monitoring equipment;
The carbon emission monitoring system also comprises a gaseous pollutant monitoring unit 2, wherein the gaseous pollutant monitoring unit 2 is used for monitoring the concentration of carbon dioxide in the flue gas in real time and providing data support for calculating the carbon dioxide emission;
Preferably, the gaseous pollutant monitoring unit 2 analyzes the flue gas sample in the sampling unit 1 by using a non-dispersive infrared (NDIR) analyzer, and distinguishes the carbon dioxide absorption spectrum from the spectrum of other gases, thereby completing the concentration measurement of carbon dioxide.
The carbon emission monitoring system further comprises a smoke emission parameter monitoring unit 3, wherein the smoke emission parameter monitoring unit 3 is used for continuously monitoring and recording key physical parameters of smoke emission, the key physical parameters comprise flow rate, temperature, pressure and humidity parameters of smoke, and necessary input data are provided for accurate calculation of carbon emission;
The smoke emission parameter monitoring unit 3 comprises a flow rate monitoring module, a temperature monitoring module, a pressure monitoring module and a humidity monitoring module;
The flow speed monitoring module is used for monitoring the flow speed of the flue gas in the discharging process in real time; specifically, a pitot tube device is adopted to capture the change of the flow rate of the flue gas;
The temperature monitoring module is used for monitoring the temperature of the flue gas in real time, evaluating the emission characteristics and the control efficiency of pollutants, and providing data support for correcting other monitoring parameters (such as flow rate and gas concentration);
The pressure monitoring module is used for monitoring the pressure state in the flue in real time, including pipeline resistance, fan working efficiency and the like; the pressure monitoring is helpful for diagnosing whether the system is blocked or leaked, and is also a basis for calculating the flow rate and adjusting the working state of the sampling system;
The humidity monitoring module is used for measuring the humidity content in the flue gas in real time, and generally adopts a high-temperature capacitance humidity sensor to measure and provide data support for calculating the emission of the wet base converted into the dry base.
The carbon emission monitoring system further comprises a data processing unit 4, wherein the data processing unit 4 is used for receiving and processing data monitored by the gaseous pollutant monitoring unit 2 and the smoke emission parameter monitoring unit 3, and calculating the actual carbon dioxide emission;
And the data processing unit 4 includes a data correction module 41, a data fusion module 42, and a data processing module 43;
specifically, the data processing module 43 optimizes the carbon emission statistical algorithm based on the carbon emission statistical algorithm and introduces a piecewise integration for ensuring accuracy of carbon dioxide emission measurement.
In the present embodiment, the data correction module 41 includes a temperature and humidity correction module, a flow rate correction module, and a cross sensitivity correction module;
The temperature and humidity correction module corrects the measured gas concentration by adopting a dry basis correction algorithm according to the monitored flue gas temperature and humidity data to obtain the dry basis concentration, wherein the dry basis correction algorithm relates to the following specific expression:
In the method, in the process of the invention, The dry basis concentration is represented, namely the actual gas concentration after the influence of moisture is subtracted; /(I)Representing a directly measured concentration of a gas containing the effects of moisture; /(I)Represents the humidity ratio, i.e. the ratio of the partial pressure of water vapor to the total air pressure, or the Relative Humidity (RH) is used directly;
The cross sensitivity correction module removes interference of other gas components on a carbon dioxide measurement result based on a cross sensitivity correction algorithm, introduces error feedback to adjust a correction coefficient, realizes iterative optimization, ensures the purity and accuracy of data, and relates to a specific expression as follows:
In the method, in the process of the invention, Representing corrected true carbon dioxide concentration; /(I)Representing the concentration of carbon dioxide on a dry basis, i.e., uncorrected, measured directly by the sensor; /(I)Representing other gases/>Is a concentration of (2); /(I)Representing gas/>Cross-sensitivity correction coefficients for carbon dioxide measurements (determined by laboratory calibration procedures); /(I)Index variables representing each gas except carbon dioxide;
the flow rate correction module corrects the actual measured flow rate by utilizing a gas state equation according to the actual monitoring temperature and pressure conditions of the flue gas and converts the actual measured flow rate into the flow rate under the standard condition, thereby eliminating measurement errors caused by working condition fluctuation and correcting the actual flow rate to the flow rate under the standard condition The specific correction process comprises the following steps:
；
In the method, in the process of the invention, Representing the carbon dioxide flow rate under standard conditions; /(I)Representing the actual measured carbon dioxide flow rate, i.e. the monitored uncorrected flue gas flow rate; /(I)Representing the actual measured flue gas temperature; /(I)The standard temperature is represented, and the value is 273.15K, namely 0 ℃; /(I)Representing the actual measured flue gas pressure; /(I)The standard atmospheric pressure was represented, and the value was 101.325kPa.
The actual flow rate of the carbon dioxide is corrected to a standard condition and used for eliminating the influence of external environmental factors, so that emission data under different conditions are comparable, and cross-region and cross-period comparative analysis is facilitated;
further, the specific steps of adjusting the cross sensitivity correction coefficient through the error feedback are as follows:
S1, specific expression based on cross sensitivity correction, for directly measuring carbon dioxide dry basis concentration Performing primary correction to obtain corrected carbon dioxide concentration/>；
In the superscriptRepresenting the result of the initial correction;
s2, after the primary correction, the sensor continuously monitors the actual emission condition in real time, including the uncorrected carbon dioxide concentration Concentration of other gases/>And corrected concentration/>；
S3, defining errorsTrue carbon dioxide concentration/>, measured for corrected concentration and higher accuracyThe difference between:
；
In the method, in the process of the invention, Representing the error; /(I)Representation relative to corrected concentration/>Higher accuracy carbon dioxide concentration of (a);
S4, according to the error Fine tuning correction coefficient/>, by iterative algorithmFor each gas/>The update of the correction coefficient involves the following expression:
；
In the method, in the process of the invention, Represents the/>Gas/>, at the time of iterationA cross sensitivity correction coefficient for carbon dioxide measurement; Represents the/> Gas/>, at the time of iterationA cross sensitivity correction coefficient for carbon dioxide measurement; /(I)Representing the iteration number; /(I)Representing a learning rate for controlling the magnitude of correction coefficient adjustment; /(I)Representing the partial derivative of the error with respect to the correction coefficient, representing the direct effect of the correction coefficient on the error; /(I)Index, each of the interfering gases other than carbon dioxide;
S5, using the updated correction coefficient And (5) correcting again:
；
In the method, in the process of the invention, Expressed in/>Correction concentration after the second iteration;
s6, repeating the steps S3 to S5 until the error is corrected Meeting a predetermined accuracy criterion or reaching a maximum number of iterations.
The whole process is a closed-loop control loop, and the optimization is continuously iterated until the expected correction precision is achieved through real-time monitoring, error calculation, correction coefficient adjustment and re-correction.
In this embodiment, the data fusion module 42 is configured to integrate multi-source data from different sensors, and perform data fusion based on a data fusion algorithm, so as to reduce errors of single-point measurement and accidental errors, thereby improving reliability and accuracy of overall data, where the data fusion algorithm specifically includes:
；
In the method, in the process of the invention, Representing the dry basis concentration of the carbon dioxide after data fusion; /(I)Representing the passage of/>Second iteration/>The dry basis concentration of carbon dioxide for each sensor; /(I)Represents the/>Weights of the individual sensors; /(I)Representing the total number of sensors; /(I)Representing an index variable;
Wherein, because the sensor may influence its measurement accuracy due to aging, pollution, environmental change, etc. in long-time operation, the current reliability of the sensor is measured by introducing performance index, and the weight is dynamically adjusted accordingly ：
；
In the method, in the process of the invention,Expressed in time/>(1 /)Dynamic weights of the individual sensors; /(I)Represents the/>The individual sensors are in timeIs determined by the estimation error variance of (a); /(I)Representing a small constant for avoiding the problem of division by zero.
The weight of each sensor in the fusion process is dynamically adjusted according to the error variance of each sensor at different time points, the sensor with small error variance (namely good performance index) can obtain higher weight, so that the measured data of the sensor can be endowed with more trust and influence when fused, otherwise, the weight is lower, and the dynamic adjustment mechanism is beneficial to improving the accuracy and the robustness of the whole data fusion.
By integrating data from different monitoring subsystems, including gaseous pollutant concentration, smoke emission parameters and the like, the data from multiple sources can be effectively fused, and a complete and consistent information basis is provided for subsequent emission calculation;
Further, the carbon emission statistical algorithm adopts a flow-concentration method and combines a time sequence to calculate the accumulated emission mass of carbon dioxide in a certain period of time, and the specific expression related to the carbon emission statistical algorithm is as follows:
；
In the method, in the process of the invention, Represents carbon dioxide over a period of time/>To/>Accumulated emission mass in; /(I)Expressed in time/>Carbon dioxide flow rate under standard conditions; /(I)Expressed in time/>The dry basis concentration of the carbon dioxide after data fusion; represents the molar mass of carbon dioxide, 44.01g/mol, for converting concentration units into mass flow units; /(I) Representing time bins, representing minute time intervals within the integration interval.
Calculating to obtain carbon dioxide in a time period by adopting a flow-concentration method and combining the corrected concentration value and the standardized flow velocityTo/>A cumulative total amount of emissions within;
conventional integration methods require a full period of time The product of flow and concentration is continuously calculated, and the calculation cost is higher when the data size is large or the change is frequent; therefore, to enhance the real-time monitoring capability, the above carbon emission statistical algorithm is optimized by introducing segment integration, which will be period/>Segmentation into/>Between cells, the time length between each cell isWherein/>Represents the/>Inter-cell, and/>；
The time interval is divided into a plurality of cells by the segment integration, and each cell assumes that the flow and the concentration are approximately constant, so that the calculation process is simplified, and the system can complete the estimation of the emission more quickly; for each cell, the approximate traffic and concentration are constant, i.eAnd/>The finer the division among cells, the more nearly constant the flow and concentration change among each cell can be considered, so that errors caused by approximation are reduced, and the calculation accuracy is improved;
the statistical arithmetic expression of the carbon emission after the segmentation optimization is as follows:
；
In the method, in the process of the invention, Represents the/>An average of flow rates among the cells; /(I)Represents the/>Average value of concentration among cells; /(I)Represents carbon dioxide over a period of time/>The accumulated emission quality in the cells, namely the sum of the emission quantity among the cells; /(I)Representing segmentation period/>Representing the number of segments into which the data is subdivided for segment integration calculation; /(I)Represents an index variable representing a segmentation period/>Obtained/>Between cells.
In a continuous monitoring system, piecewise integration enables the system to update emissions estimates in real time or near real time, which is critical for immediate feedback and quick response to emissions anomalies; by updating inter-cell data regularly, the system can recognize the change of emission trend more quickly and send out early warning in time;
In the discharging process, the flow and the concentration often change in a nonlinear way along with time, and if the average value of a long period of time is adopted for calculation, larger errors can be accumulated because instantaneous changes are ignored; the sectional integration makes the estimation of the flow and the concentration more close to the actual instantaneous value by reducing the time window of analysis, thereby improving the accuracy of the emission estimation;
The introduction of the segmented integration method is an efficient and practical strategy for processing continuous time series data, especially for large-scale carbon emission monitoring and management, by improving the calculation efficiency, enhancing the flexibility and accuracy of data processing and supporting real-time monitoring.
The carbon emission monitoring system further comprises an early warning unit 5, wherein the early warning unit 5 compares monitored data with preset emission standards in real time according to the preset emission standards and alarms abnormal data.
In this embodiment, the early warning unit 5 includes a threshold setting module, a real-time comparison module, and an alarm triggering module;
The threshold setting module is used for setting emission standards, namely, the emission standards are an upper limit of carbon dioxide concentration and an upper limit of carbon dioxide emission, and the upper limit of carbon dioxide concentration and the upper limit of carbon dioxide emission are used as thresholds;
the real-time comparison module is used for comparing the monitored data with a preset emission standard in real time, identifying emission data exceeding a threshold value, and judging whether the carbon emission has potential illegal emission risk or not;
The alarm triggering module is based on the analysis result of the real-time comparison module, and once the data analysis result shows that the emission data exceeds the threshold value, the module immediately starts an alarm mechanism, wherein the alarm mechanism comprises an audible alarm, a visual alarm (such as flashing light), a short message or an email, and the alarm mechanism informs a preset operator or manager to ensure that the information is rapidly conveyed.
Example 2:
The difference between the embodiment 2 and the embodiment 1 of the present invention is that the embodiment describes a static mechanical property collection and analysis method used by a carbon emission monitoring system based on CEMS.
A CEMS-based carbon emission monitoring method for use in any of the CEMS-based carbon emission monitoring systems described above, comprising the steps of:
S10.1, firstly, extracting a smoke sample from a flue through a sampling unit 1, and monitoring and recording parameters of smoke flow rate, temperature, pressure and humidity in the smoke sample through a smoke emission parameter monitoring unit 3;
S10.2, simultaneously, analyzing a smoke sample by a gaseous pollutant monitoring unit 2 through a non-dispersive infrared (NDIR) analyzer, distinguishing a carbon dioxide absorption spectrum from the spectrums of other gases, and realizing concentration measurement of carbon dioxide;
s10.3, receiving and processing data monitored by the gaseous pollutant monitoring unit 2 and the smoke emission parameter monitoring unit 3 through the data processing unit 4, wherein the original data are adjusted through the data correction module 41 so as to eliminate environmental factors and equipment cross influence and ensure the purity and accuracy of a measurement result;
Integrating the multi-source data by the data fusion module 42, introducing performance indexes to dynamically adjust the sensor weight, selectively relying on high-precision data sources based on the performance indexes, and improving the reliability of final emission calculation;
The data processing module 43 combines a flow-concentration method with time sequence analysis, and the total carbon dioxide emission amount is calculated in real time through optimization of a sectional integration method, so that the efficiency of processing a large amount of data is improved;
And S10.4, finally, comparing the real-time data with a preset threshold value through the early warning unit 5 for monitoring whether the emission behavior is in compliance, and starting an alarm mechanism through the alarm triggering module by the early warning unit 5 once the real-time carbon emission data is detected to exceed the preset threshold value, so that timely taking corrective measures is ensured, and the occurrence of illegal emission events is effectively prevented.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A CEMS-based carbon emission monitoring system, comprising:
The sampling unit (1) is responsible for extracting a flue gas sample from the flue and is used for providing data support for subsequent pollutant concentration analysis, emission parameter measurement and final carbon emission calculation;
The system comprises a gaseous pollutant monitoring unit (2), wherein the gaseous pollutant monitoring unit (2) is used for monitoring the concentration of carbon dioxide in flue gas in real time and providing data support for calculating the carbon dioxide emission;
The system comprises a flue gas emission parameter monitoring unit (3), wherein the flue gas emission parameter monitoring unit (3) is used for continuously monitoring and recording key physical parameters of flue gas emission, and the key physical parameters comprise flow rate, temperature, pressure and humidity parameters of flue gas;
The data processing unit (4) is used for receiving and processing the data monitored by the gaseous pollutant monitoring unit (2) and the smoke emission parameter monitoring unit (3) and calculating the actual carbon dioxide emission;
And the data processing unit (4) comprises a data correction module (41), a data fusion module (42) and a data processing module (43);
the data processing module (43) optimizes the carbon emission statistical algorithm based on the carbon emission statistical algorithm and introduces a sectional integral for ensuring the accuracy of carbon dioxide emission measurement;
the early warning unit (5), the early warning unit (5) compares the monitored data with the preset emission standard in real time according to the preset emission standard, and alarms the abnormal data.
2. The CEMS-based carbon emission monitoring system of claim 1, wherein: the gaseous pollutant monitoring unit (2) adopts a non-dispersive infrared analyzer to analyze the flue gas sample in the sampling unit (1) and distinguish the carbon dioxide absorption spectrum from the spectrum of other gases, thereby completing the concentration measurement of carbon dioxide.
3. The CEMS-based carbon emission monitoring system of claim 1, wherein: the smoke emission parameter monitoring unit (3) comprises a flow rate monitoring module, a temperature monitoring module, a pressure monitoring module and a humidity monitoring module;
The flow speed monitoring module is used for monitoring the flow speed of the flue gas in the discharging process in real time; the temperature monitoring module is used for monitoring the temperature of the flue gas in real time; the pressure monitoring module is used for monitoring the pressure state in the flue in real time; the humidity monitoring module is used for measuring the humidity content in the flue gas in real time.
4. The CEMS-based carbon emission monitoring system of claim 1, wherein: the data correction module (41) comprises a temperature and humidity correction module, a flow rate correction module and a cross sensitivity correction module;
the temperature and humidity correction module corrects the measured gas concentration by adopting a dry basis correction algorithm according to the monitored flue gas temperature and humidity data to obtain dry basis concentration, wherein the dry basis correction algorithm comprises the following specific expression:
In the method, in the process of the invention, Represents the dry basis concentration; /(I)Representing a directly measured concentration of a gas containing the effects of moisture; /(I)Representing the humidity ratio;
The cross sensitivity correction module removes interference of other gas components on a carbon dioxide measurement result based on a cross sensitivity correction algorithm, and introduces an error feedback adjustment correction coefficient, wherein the cross sensitivity correction algorithm relates to the following specific expression:
In the method, in the process of the invention, Representing corrected true carbon dioxide concentration; /(I)Representing the concentration of the carbon dioxide dry basis directly measured by the sensor; /(I)Representing other gases/>Is a concentration of (2); /(I)Representing gas/>A cross sensitivity correction coefficient for carbon dioxide measurement; /(I)Representing an index variable;
the flow rate correction module corrects the actual flow rate to the flow rate under the standard condition according to the actual monitoring temperature and pressure conditions of the flue gas The specific correction process comprises the following steps:
In the method, in the process of the invention, Representing the carbon dioxide flow rate under standard conditions; /(I)Representing the actual measured carbon dioxide flow rate; /(I)Representing the actual measured flue gas temperature; /(I)Representing a standard temperature; /(I)Representing the actual measured flue gas pressure; /(I)Indicating standard atmospheric pressure.
5. The CEMS-based carbon emission monitoring system of claim 4, wherein the step of adjusting the cross-sensitivity correction factor by the error feedback is:
S1, specific expression based on cross sensitivity correction, for directly measuring carbon dioxide dry basis concentration Performing primary correction to obtain corrected carbon dioxide concentration/>；
In the superscriptRepresenting the result of the initial correction;
s2, after the primary correction, the sensor continuously monitors the actual emission condition in real time, including the uncorrected carbon dioxide concentration Concentration of other gases/>And corrected concentration/>；
S3, defining errorsTrue carbon dioxide concentration/>, measured for corrected concentration and higher accuracyThe difference between:
In the method, in the process of the invention, Representing the error; /(I)Representation relative to corrected concentration/>Higher accuracy carbon dioxide concentration of (a);
S4, according to the error Fine tuning correction coefficient/>, by iterative algorithmFor each gas/>The update of the correction coefficient involves the following expression:
；
In the method, in the process of the invention, Represents the/>Gas/>, at the time of iterationA cross sensitivity correction coefficient for carbon dioxide measurement; /(I)Represents the/>Gas/>, at the time of iterationA cross sensitivity correction coefficient for carbon dioxide measurement; /(I)Representing the iteration number; /(I)Representing a learning rate; /(I)Representing the partial derivative of the error with respect to the correction coefficient, representing the direct effect of the correction coefficient on the error; /(I)Representing an index;
S5, using the updated correction coefficient And (5) correcting again:
；
In the method, in the process of the invention, Expressed in/>Correction concentration after the second iteration;
s6, repeating the steps S3 to S5 until the error is corrected Meeting a predetermined accuracy criterion or reaching a maximum number of iterations.
6. The CEMS-based carbon emission monitoring system of claim 5, wherein the data fusion module (42) is configured to integrate multi-source data from different sensors and perform data fusion based on a data fusion algorithm, the data fusion algorithm specifically being:
；
In the method, in the process of the invention, Representing the dry basis concentration of the carbon dioxide after data fusion; /(I)Representing the passage of/>Second iteration/>The dry basis concentration of carbon dioxide for each sensor; /(I)Represents the/>Weights of the individual sensors; /(I)Representing the total number of sensors; /(I)Representing an index variable;
Wherein, the current credibility of the sensor is measured by introducing performance index, and the weight is dynamically adjusted according to the current credibility ：
；
In the method, in the process of the invention,Expressed in time/>(1 /)Dynamic weights of the individual sensors; /(I)Represents the/>Individual sensor at time/>Is determined by the estimation error variance of (a); /(I)Representing a small constant.
7. The CEMS-based carbon emission monitoring system of claim 6, wherein the carbon emission statistical algorithm calculates the accumulated emission mass of carbon dioxide over a period of time using a flow-concentration method in combination with a time series, and the specific expression involved in the carbon emission statistical algorithm is:
；
In the method, in the process of the invention, Represents carbon dioxide over a period of time/>To/>Accumulated emission mass in; /(I)Expressed in time/>Carbon dioxide flow rate under standard conditions; /(I)Expressed in time/>The dry basis concentration of the carbon dioxide after data fusion; /(I)Represents the molar mass of carbon dioxide; /(I)Representing time bins, representing minute time intervals within the integration interval.
8. The CEMS-based carbon emission monitoring system of claim 7, wherein introducing segment integration optimizes the carbon emission statistical algorithm for a time periodSegmentation into/>Between cells, the time length between each cell isWherein/>Represents the/>Inter-cell, and/>；
The statistical arithmetic expression of the carbon emission after the segmentation optimization is as follows:
；
In the method, in the process of the invention, Represents the/>An average of flow rates among the cells; /(I)Represents the/>Average value of concentration among cells; Represents carbon dioxide over a period of time/> Accumulated emission mass in; /(I)Representing segmentation period/>Is a total number of cells; /(I)Representing the index variable.
9. The CEMS-based carbon emission monitoring system of claim 1, wherein: the early warning unit (5) comprises a threshold setting module, a real-time comparison module and an alarm triggering module;
The threshold setting module is used for setting an upper limit of carbon dioxide concentration and an upper limit of carbon dioxide emission;
the real-time comparison module is used for comparing the monitored data with a preset emission standard in real time, identifying emission data exceeding a threshold value, and judging whether the carbon emission has potential illegal emission risk or not;
The alarm triggering module is based on the analysis result of the real-time comparison module, and once the data analysis result shows that the emission data exceeding the threshold value is displayed, the module immediately starts an alarm mechanism.
10. A CEMS-based carbon emission monitoring method for use in a CEMS-based carbon emission monitoring system according to any one of claims 1 to 9, comprising the steps of:
S10.1, firstly, extracting a smoke sample from a flue through a sampling unit (1), and monitoring and recording parameters of smoke flow rate, temperature, pressure and humidity in the smoke sample through a smoke emission parameter monitoring unit (3);
s10.2, simultaneously, analyzing a smoke sample by a gaseous pollutant monitoring unit (2) through a non-dispersive infrared analyzer, distinguishing a carbon dioxide absorption spectrum from spectrums of other gases, and realizing concentration measurement of carbon dioxide;
S10.3, receiving and processing data monitored by the gaseous pollutant monitoring unit (2) and the smoke emission parameter monitoring unit (3) through the data processing unit (4), wherein the original data are adjusted through the data correction module (41) so as to eliminate environmental factors and equipment cross influence and ensure the purity and accuracy of a measurement result;
Integrating multi-source data by the data fusion module (42), introducing performance indexes to dynamically adjust sensor weights, selectively relying on high-precision data sources based on the performance indexes, and improving the reliability of final emission calculation;
The data processing module (43) combines a flow-concentration method with time sequence analysis, and the total carbon dioxide emission amount is calculated in real time through optimization of a sectional integration method, so that the efficiency of processing a large amount of data is improved;
And S10.4, finally, comparing the real-time data with a preset threshold value through the early warning unit (5) for monitoring whether the emission behavior is in compliance, and starting an alarm mechanism through the alarm triggering module by the early warning unit (5) once the real-time carbon emission data is detected to exceed the preset threshold value.
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