Thermal power plant carbon emission monitoring method based on laser-induced breakdown spectroscopy technology

Abstract

The invention relates to the technical field of carbon emission monitoring. The invention relates to a thermal power plant carbon emission monitoring method based on a laser-induced breakdown spectroscopy technology. The method comprises the following steps: S1, determining a monitoring point position of coal as fired; s2, installing an LIBS system at the monitoring point position, and performing data processing and data calibration on monitoring data acquired by the LIBS system; through the LIBS coal detection and coal quantity data real-time measurement matching technology, a thermal power plant management end can formulate an accurate adjustment scheme according to real-time data, carbon emission is controlled in a targeted mode, the efficiency and accuracy of carbon emission control are improved, and the real-time coal flow emission amount accumulation model and the carbon emission intensity model are established, so that the coal flow emission amount real-time accumulation model is established. The scheme execution is automatically adjusted, the labor cost and the operation cost can be reduced, the thermal power plant can be flexibly adjusted according to real-time requirements, excessive or insufficient energy consumption is avoided, and therefore the cost is saved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission monitoring, and in particular to a method for monitoring carbon emission in a thermal power plant based on laser induced breakdown spectroscopy technology. Background Art

[0002] At present, the accuracy, reliability and timeliness of carbon emission data are extremely important. Most carbon emission data are calculated using accounting methods, and the calculation and reporting of carbon emission data are carried out through the online system stipulated by the state. The accounting process involves many parameters, multiple data sources, long processes, multiple detection equipment, multiple personnel, large workload, and long time, which is prone to human negligence or artificial falsification. The online monitoring of carbon dioxide in flue gas that is being piloted has the problems of difficult flow monitoring and frequent changes in concentration measurement, resulting in inaccurate measurement data.

[0003] The principle of laser induced breakdown spectroscopy (LIBS) technology is to excite samples through high-energy pulsed lasers to generate plasma and detect the emission spectrum of the plasma. By establishing a mathematical relationship between characteristic spectral lines and element concentrations, the elemental composition content of coal is finally obtained. It has the advantages of low sample preparation requirements, fast measurement speed, multi-element measurement, long-distance analysis, safety and no radiation. It is called the "future chemical analysis superstar" and has shown great application potential in many fields such as coal, minerals, and metallurgy. It is also known as one of the most competitive online analysis technologies for coal quality. Therefore, a carbon emission monitoring method for thermal power plants based on laser induced breakdown spectroscopy technology is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a method for monitoring carbon emissions from a thermal power plant based on laser induced breakdown spectroscopy technology to solve the problems raised in the above background technology.

[0005] To achieve the above purpose, a method for monitoring carbon emissions from a thermal power plant based on laser induced breakdown spectroscopy is provided, comprising the following steps: S1. Determine the monitoring points of coal entering the furnace; S2. Install the LIBS system at the monitoring point and perform data processing and data calibration on the monitoring data obtained by the LIBS system; S3. Extract the carbon element data in the sample based on the monitoring data obtained by the LIBS system, and extract the operating parameters of the conveyor belt at the same time, and establish a real-time accumulation model of coal flow emissions and a carbon emission intensity model based on the carbon element data and operating parameters; S4. Perform early warning analysis on coal flow emissions and carbon emission intensity based on carbon element data and operating parameters, obtain coal flow values ​​that will lead to excessive energy consumption based on the analysis results, and then use the coal flow values ​​as early warning thresholds; S5. Perform numerical comparison between the warning threshold, the real-time cumulative model of coal flow emissions, and the carbon emission intensity model. When the emissions and emission intensity exceed the warning threshold, perform abnormal adjustment analysis based on the emissions and emission intensity, obtain the adjustment plan, and then upload the adjustment plan to the thermal power plant management end to adjust the carbon emissions.

[0006] As a further improvement of the technical solution, S1 obtains the detection range of the LIBS system and collects the transportation range of the coal entering the furnace, then performs coverage analysis on the detection range in combination with the transportation range, and determines the monitoring point of the coal entering the furnace based on the analysis results.

[0007] As a further improvement of the present technical solution, the LIBS system in S2 is composed of a laser emitter, a lens, a lens optical element, a sample stage, and a spectrometer. The sample composition is determined by inducing plasma from the sample using laser and analyzing its emission spectrum.

[0008] As a further improvement of the technical solution, the step S2 includes the following: S2.1. Install the LIBS system at the monitoring point, and use the LIBS system to obtain monitoring data during the transportation of coal entering the furnace; S2.2. Use data processing software to pre-process the monitoring data obtained by the LIBS system, and then calibrate the LIBS system by using standard samples combined with the monitoring data.

[0009] As a further improvement of this technical solution, the formula for establishing the real-time cumulative model of coal flow emissions in S3 is as follows: Q=v×t×C Among them, Q represents the coal flow emission, v represents the conveying speed, the unit can be tons / hour, cubic meters / hour, etc., which represents the volume or mass of coal conveyed per unit time, t represents the conveying time, the unit is usually hours, which represents the time of coal conveying, and C represents the carbon content of coal, which is usually expressed as a percentage or mass fraction.

[0010] As a further improvement of this technical solution, the formula for establishing the carbon emission intensity model in S3 is as follows: Among them, I represents carbon emission intensity, E represents the amount of carbon emitted, that is, the total amount of carbon emitted per unit time, and M represents the coal input, that is, the total amount of coal delivered to the system per unit time.

[0011] As a further improvement of the technical solution, the step of S4 is as follows: S4.1. Conduct early warning analysis on coal flow emissions and carbon emission intensity based on carbon element data and operating parameters to obtain coal flow emissions and carbon emission intensity that may lead to excessive energy consumption in carbon emissions; S4.2. Threshold screening is performed on the coal flow emissions and carbon emission intensity that will lead to excessive energy consumption in carbon emissions, and the minimum coal flow emissions and the minimum carbon emission intensity are obtained and merged into a coal flow value, and then the coal flow value is used as a warning threshold.

[0012] As a further improvement of the technical solution, the step of S5 is as follows: S5.1. Numerical comparison between the early warning threshold and the real-time cumulative model of coal flow emissions and the carbon emission intensity model; S5.2. When the emission volume and emission intensity exceed the warning threshold, an abnormal adjustment analysis is performed based on the emission volume and emission intensity to obtain an adjustment plan, which is then uploaded to the thermal power plant management terminal to adjust carbon emissions; S5.3. When the emission volume and emission intensity do not exceed the warning threshold, continue monitoring.

[0013] As a further improvement of the technical solution, S5.2 establishes a data transmission protocol between the LIBS system and the thermal power plant management end. The adjustment plan generated first is sent by the LIBS system to the thermal power plant management end to control carbon emissions.

[0014] Compared with the prior art, the present invention has the following beneficial effects: In this thermal power plant carbon emission monitoring method based on laser induced breakdown spectroscopy technology, carbon emissions are monitored and early warnings are issued through the real-time measurement and matching technology of LIBS coal quality detection and coal quantity data. The thermal power plant management can formulate accurate adjustment plans, control carbon emissions in a targeted manner, and improve the accuracy and efficiency of carbon emission control. By establishing a real-time cumulative model of coal flow emissions, automatic analysis and generation of analysis reports, unmanned operation can be achieved, and labor costs and operating costs can be reduced. Thermal power plants can be flexibly adjusted according to real-time needs to create carbon benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is the overall flow chart of the present invention; Figure 2 A flowchart of the present invention for calibrating a LIBS system using standard samples combined with monitoring data; Figure 3 This is a flowchart of the present invention using the coal flow value as an early warning threshold; Figure 4 A flowchart of adjusting carbon emissions according to the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1-Figure 4 As shown, the purpose of this embodiment is to provide a method for monitoring carbon emissions from a thermal power plant based on laser induced breakdown spectroscopy technology, comprising the following steps: S1. Determine the monitoring points of coal entering the furnace; S1 first detects the coal quality of the incoming coal and obtains the transportation route of the incoming coal, then analyzes the detection range and the transportation route, and determines the monitoring point of the incoming coal according to the analysis results. The working steps are as follows: Testing requirements for incoming coal: Determine the elements and industrial analysis parameters to be tested based on the key requirements of the thermal power plant; Transportation route: determine the transportation process of coal entering the furnace and the sampling situation; Monitoring area: Determine the area that needs to be monitored based on the transportation route and the source of the coal entering the furnace; Sampling points: Select representative sampling points in the monitoring area to cover the testing requirements of coal entering the furnace; Coverage analysis: Perform coverage analysis on the detection range of the LIBS system and the transportation range of the collected coal entering the furnace to determine the overlapping areas and uncovered areas between the two. Priority areas: Priority is given to selecting locations covering the sampling room and beyond as monitoring points; Frequency setting: Ensure that the monitoring frequency matches the speed of the coal belt entering the furnace to improve the comprehensiveness and representativeness of the monitoring.

[0018] S2. Install the LIBS system at the monitoring point and perform data processing and data calibration on the monitoring data obtained by the LIBS system; The LIBS system in S2 is composed of a laser emitter, a lens, a lens optical element, a sample stage, and a spectrometer. The sample composition is determined by inducing plasma from the sample using laser and analyzing its emission spectrum.

[0019] Laser transmitter: used to generate high-energy laser pulses to excite samples to produce plasma.

[0020] Mirrors, lens optical elements: used to focus lasers and collect emitted light signals.

[0021] Sample stage: used to capture the spectral signal emitted by the plasma generated by the sample, usually a CCD or CMOS detector.

[0022] Spectrometer: Used to analyze the spectral signal emitted by the sample, usually using a grating spectrometer or other types of spectral analysis instruments.

[0023] The steps of S2 include the following: S2.1. Install the LIBS system at the monitoring point, and use the LIBS system to obtain monitoring data during the transportation of coal entering the furnace. The working steps are as follows: Choose a suitable location: Choose a suitable location near the monitoring point to install the LIBS system, ensure that it can effectively cover the transportation path of the coal entering the furnace, fix the LIBS system on a bracket or platform to ensure that it is stable and reliable, and adjust the direction of the LIBS system so that it can accurately monitor the coal entering the furnace; Real-time monitoring: The LIBS system can monitor the chemical composition of the coal entering the furnace in real time during transportation, and record the monitored data, including element type, concentration, time and other information.

[0024] S2.2. Use data processing software to pre-process the monitoring data obtained by the LIBS system, and then calibrate the LIBS system by using standard samples combined with the monitoring data. The working steps are as follows: Data cleaning: remove outliers, missing values, etc. to clean the monitoring data to ensure the accuracy and completeness of the data; Data smoothing: Smoothing the monitoring data to reduce noise and fluctuations and make the data more stable; Data standardization: Standardize the monitoring data so that data with different characteristics have the same scale and range, which is convenient for subsequent processing and comparison; Outlier processing: For outliers, methods such as average, median or interpolation can be used to process them to ensure the reliability and rationality of the data; Select standard samples: Select a series of standard samples with known chemical composition, covering the elements and compounds that may be present in the incoming coal; LIBS system measurement: Use the LIBS system to measure the standard sample and obtain its spectral data and corresponding chemical composition; Establish a calibration model: Use the monitored spectral data and the chemical composition of the standard sample to establish a calibration model. Linear regression can be used. The formula is as follows: Among them, C i is the chemical composition of the i-th standard sample, X1,X2,...,X n are the features in the spectral data measured by LIBS, a0, a1, a2, ..., a n is the regression coefficient, ε is the error term, and the model training process usually involves collecting samples with known chemical composition Ci and the corresponding measured features X1,X2,...,X n Then, use this data to fit the regression model and find the best coefficients a0, a1, a2, ..., a n , so that the model's predictions are as close as possible to the actual observed values. Once the model is trained, it can be used to predict the chemical composition C of new samples i； Model validation: The established calibration model is validated to evaluate its accuracy and predictive ability. At the same time, new LIBS measurement data are processed and calculated according to the established calibration model to obtain the corresponding chemical composition estimates.

[0025] S3. Extract the carbon element data in the sample based on the monitoring data obtained by the LIBS system, and extract the operating parameters of the conveyor belt at the same time. Establish a real-time cumulative model of coal flow emissions and a carbon emission intensity model based on the carbon element data and operating parameters. The working steps are as follows: Extract carbon element data: Extract spectral features related to carbon element or direct carbon element content data from the monitoring data obtained by the LIBS system; Extracting conveyor belt operating parameters: Extracting operating parameters such as conveying speed, conveying volume, etc. from the conveyor belt sensor or monitoring system; Establish a real-time cumulative model for coal flow emissions: Use carbon element data and the operating parameters of the conveyor belt to establish a real-time cumulative model for coal flow emissions. This model can be a simple cumulative model based on the principle of material balance, taking into account factors such as coal input and output, as well as conveying time and speed. The formula is as follows: Among them, Q represents the coal flow discharge, v represents the conveying speed, the unit can be tons / hour, cubic meters / hour, etc., which represents the volume or mass of coal conveyed per unit time, t represents the conveying time, the unit is usually hours, which represents the time of conveying coal, and C represents the carbon content of coal, which is usually expressed as a percentage or mass fraction; The conveying speed v represents the amount of coal conveyed per unit time. If the amount of coal conveyed per hour is known, the total amount of coal conveyed in a given time period can be calculated by multiplying it by the conveying time t. The conveying time t represents the time for conveying coal. By multiplying the conveying speed by the conveying time, the total amount of coal conveyed during this period can be obtained. The carbon content C of coal represents the carbon content per unit of coal. By multiplying the total amount of coal conveyed by the carbon content of coal, the total amount of carbon in the coal conveyed during this period can be obtained, that is, the coal flow emission; Establish a carbon emission intensity model: Use carbon element data and conveyor belt operating parameters to establish a carbon emission intensity model. This model can be based on the analysis of coal combustion characteristics and emission factors, taking into account factors such as coal combustion efficiency and emission factors. The formula is as follows: Among them, I represents carbon emission intensity, E represents the amount of carbon emitted, that is, the total amount of carbon emitted per unit time, and M represents the amount of coal input, that is, the total amount of coal delivered to the system per unit time; By dividing the amount of carbon emitted E by the amount of coal input M, we can get the carbon emissions generated per unit of coal input per unit time. This value is the carbon emission intensity, which indicates the intensity level of carbon emissions generated per unit of coal input; Data verification: Use historical data or field measurement data to verify the established model, evaluate its accuracy and reliability, and adjust and optimize the model based on the verification results to improve its predictive ability and applicability; Real-time monitoring: Apply the established model to the real-time monitoring system to obtain carbon element data and conveyor belt operating parameters in real time, and calculate coal flow emissions and carbon emission intensity.

[0026] S4. Perform early warning analysis on coal flow emissions and carbon emission intensity based on carbon element data and operating parameters, obtain coal flow values ​​that will lead to excessive energy consumption based on the analysis results, and then use the coal flow values ​​as early warning thresholds; The steps of S4 are as follows: S4.1. Conduct early warning analysis on coal flow emissions and carbon emission intensity based on carbon element data and operating parameters to obtain coal flow emissions and carbon emission intensity that may lead to excessive energy consumption in carbon emissions; S4.2. Threshold screening will be performed on the coal flow emissions and carbon emission intensity that will lead to excessive energy consumption in carbon emissions. The minimum coal flow emissions and the minimum carbon emission intensity will be combined into the coal flow value, and then the coal flow value will be used as the warning threshold. The working steps are as follows: Data analysis: Analyze historical data, environmental standards and factory regulations to understand relevant carbon emission limits and safety standards; Risk assessment: Evaluate the potential risks of coal flow emissions and carbon emission intensity to the environment, health, etc., and determine the factors that should be considered for the minimum threshold; Determine the minimum threshold: Based on the data analysis and risk assessment results, determine the minimum coal flow emissions and the minimum carbon emission intensity thresholds that lead to excessive energy consumption in carbon emissions; Combined threshold: Combine the minimum coal flow emissions and the minimum carbon emission intensity into a comprehensive coal flow value as the warning threshold.

[0027] S5. Perform numerical comparison between the warning threshold, the real-time cumulative model of coal flow emissions, and the carbon emission intensity model. When the emissions and emission intensity exceed the warning threshold, perform abnormal adjustment analysis based on the emissions and emission intensity, obtain the adjustment plan, and then upload the adjustment plan to the thermal power plant management end to adjust the carbon emissions.

[0028] The steps of S5 are as follows: S5.1. Numerical comparison between the early warning threshold and the real-time cumulative model of coal flow emissions and the carbon emission intensity model; S5.2. When the emission volume and emission intensity exceed the warning threshold, an abnormal adjustment analysis is performed based on the emission volume and emission intensity to obtain an adjustment plan, which is then uploaded to the thermal power plant management terminal to adjust carbon emissions; S5.3. When the emission volume and emission intensity do not exceed the warning threshold, continue monitoring. The working steps are as follows: Real-time monitoring and model comparison: The coal flow emissions and carbon emission intensity data obtained through the real-time monitoring system are compared with the warning thresholds; Abnormal adjustment analysis: When the emission volume and emission intensity exceed the warning threshold, abnormal adjustment analysis is carried out to determine how to adjust the coal production or combustion process to reduce the emission volume and emission intensity; Formulate adjustment plans: Based on the results of abnormal adjustment analysis, formulate specific adjustment plans, including adjusting coal-fired equipment parameters, adding pollutant treatment facilities, and other measures; Upload to the management end: upload the adjustment plan to the thermal power plant management end and notify the relevant responsible personnel to implement the adjustment plan; Continuous monitoring: For situations that do not exceed the warning threshold, continuous monitoring is carried out to ensure that carbon emissions are within a safe range.

[0029] S5.2 establishes a data transmission protocol between the LIBS system and the thermal power plant management end. The adjustment plan generated first is sent by the LIBS system to the thermal power plant management end to control carbon emissions. The working steps are as follows: Protocol formulation: formulate a clear data transmission protocol, defining the data transmission format, communication method, security requirements, etc. Communication protocol selection: Select HTTP to ensure the stability and reliability of data transmission; Data format regulations: Determine the data format of the adjustment plan, including adjustment content, adjustment parameters, execution time and other information, so that the thermal power plant management end can correctly parse and execute it; Security mechanism setting: Establish a security mechanism for data transmission, including data encryption, identity authentication, access control, etc., to ensure the security and confidentiality of data transmission; Interface development: Develop corresponding interfaces on the LIBS system and the thermal power plant management side to realize data transmission and reception functions.

[0030] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for monitoring carbon emissions from thermal power plants based on laser induced breakdown spectroscopy technology, characterized in that: The following steps are involved: S1. Determine the monitoring points of coal entering the furnace; S2. Install the LIBS system at the monitoring point and perform data processing and data calibration on the monitoring data obtained by the LIBS system; S3. Extract the carbon element data in the sample based on the monitoring data obtained by the LIBS system, and extract the operating parameters of the conveyor belt at the same time, and establish a real-time accumulation model of coal flow emissions and a carbon emission intensity model based on the carbon element data and operating parameters; S4. Perform early warning analysis on coal flow emissions and carbon emission intensity based on carbon element data and operating parameters, obtain coal flow values ​​that will lead to excessive energy consumption based on the analysis results, and then use the coal flow values ​​as early warning thresholds; S5. Perform numerical comparison between the warning threshold, the real-time cumulative model of coal flow emissions, and the carbon emission intensity model. When the emissions and emission intensity exceed the warning threshold, perform abnormal adjustment analysis based on the emissions and emission intensity, obtain the adjustment plan, and then upload the adjustment plan to the thermal power plant management end to adjust the carbon emissions.

2. The method for monitoring carbon emissions from thermal power plants based on laser induced breakdown spectroscopy technology according to claim 1 is characterized in that: The S1 obtains the detection range of the LIBS system and the transportation range of the coal entering the furnace, and then performs coverage analysis on the detection range in combination with the transportation range, and determines the monitoring point of the coal entering the furnace according to the analysis results.

3. The method for monitoring carbon emissions from thermal power plants based on laser induced breakdown spectroscopy technology according to claim 1 is characterized in that: The LIBS system in S2 is composed of a laser emitter, a lens, a lens optical element, a sample stage, and a spectrometer. The sample composition is determined by inducing plasma from the sample using laser and analyzing its emission spectrum.

4. The method for monitoring carbon emissions from thermal power plants based on laser induced breakdown spectroscopy technology according to claim 1 is characterized in that: The steps of S2 include the following: S2.

1. Install the LIBS system at the monitoring point, and use the LIBS system to obtain monitoring data during the transportation of coal entering the furnace; S2.

2. Use data processing software to pre-process the monitoring data obtained by the LIBS system, and then calibrate the LIBS system by using standard samples combined with the monitoring data.

5. The method for monitoring carbon emissions from thermal power plants based on laser induced breakdown spectroscopy technology according to claim 1 is characterized in that: The formula for establishing the real-time cumulative model of coal flow emissions in S3 is as follows: Q=v×t×C Among them, Q represents the coal flow emission, v represents the conveying speed, the unit can be tons / hour, cubic meters / hour, etc., which represents the volume or mass of coal conveyed per unit time, t represents the conveying time, the unit is usually hours, which represents the time of coal conveying, and C represents the carbon content of coal, which is usually expressed as a percentage or mass fraction.

6. The method for monitoring carbon emissions from thermal power plants based on laser induced breakdown spectroscopy technology according to claim 1, characterized in that: The formula for establishing the carbon emission intensity model of S3 is as follows: Among them, I represents carbon emission intensity, E represents the amount of carbon emitted, that is, the total amount of carbon emitted per unit time, and M represents the coal input, that is, the total amount of coal delivered to the system per unit time.

7. The method for monitoring carbon emissions from thermal power plants based on laser induced breakdown spectroscopy technology according to claim 1 is characterized in that: The steps of S4 are as follows: S4.

1. Conduct early warning analysis on coal flow emissions and carbon emission intensity based on carbon element data and operating parameters to obtain coal flow emissions and carbon emission intensity that may lead to excessive energy consumption in carbon emissions; S4.

2. Threshold screening is performed on the coal flow emissions and carbon emission intensity that will lead to excessive energy consumption in carbon emissions, and the minimum coal flow emissions and the minimum carbon emission intensity are obtained and merged into a coal flow value, and then the coal flow value is used as the warning threshold.

8. The method for monitoring carbon emissions from thermal power plants based on laser induced breakdown spectroscopy technology according to claim 1, characterized in that: The steps of S5 are as follows: S5.

1. Numerical comparison between the early warning threshold and the real-time cumulative model of coal flow emissions and the carbon emission intensity model; S5.

2. When the emission volume and emission intensity exceed the warning threshold, an abnormal adjustment analysis is performed based on the emission volume and emission intensity to obtain an adjustment plan, which is then uploaded to the thermal power plant management terminal to adjust carbon emissions; S5.

3. When the emission volume and emission intensity do not exceed the warning threshold, continue monitoring.

9. The method for monitoring carbon emissions from thermal power plants based on laser induced breakdown spectroscopy technology according to claim 8, characterized in that: S5.2 establishes a data transmission protocol between the LIBS system and the thermal power plant management end. The adjustment plan generated first is sent by the LIBS system to the thermal power plant management end to control carbon emissions.

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