Coal-fired power plant carbon emission index real-time monitoring method and system

Abstract

The invention discloses a coal-fired power plant carbon emission index real-time monitoring method and a coal-fired power plant carbon emission index real-time monitoring system, and relates to the field of carbon emission measurement. According to the method, firstly, the detection accuracy of the carbon content of coal before entering a furnace is optimized on the basis of adjusting the number of LIBS detection probes arranged on a coal entering belt; the coal quality before entering the furnace is replaced according to the detected carbon content data of the coal quality before entering the furnace and in combination with the corresponding power consumption scene, so that fuel is saved, and the power generation efficiency is improved; finally, combustion parameters of the corresponding combustors are adjusted according to the carbon content data of the replaced coal before entering the furnace, sufficient combustion of the coal is guaranteed, fuel is fully utilized, the proportion of harmful gas during final carbon emission is reduced, and the problem that the coal is combusted in a traditional fixed mode, so that the combustion efficiency is greatly improved is solved. The problems of fuel waste and low power generation efficiency caused by the fact that the to-be-combusted coal quality cannot be dynamically adjusted based on the power consumption scene are solved.

Description

Technical Field

[0001] The present invention belongs to the field of carbon emission measurement. Specifically, it particularly relates to a method and system for real-time monitoring of carbon emission indicators in coal-fired power plants. Background Art

[0002] Currently, when burners are used to burn coal quality in coal-fired power plants, it is impossible to dynamically adjust the coal quality to be burned according to the electricity consumption scenarios (such as peak electricity consumption periods and low electricity consumption periods, etc.), resulting in fuel waste and low power generation efficiency. Summary of the Invention

[0003] In view of the problems in the related art, the present invention provides a method and system for real-time monitoring of carbon emission indicators in coal-fired power plants to overcome the above-mentioned technical problems existing in the existing related technologies.

[0004] To solve the above technical problems, the present invention is realized through the following technical solutions: The present invention provides a method for real-time monitoring of carbon emission indicators in coal-fired power plants, including the following steps: S1. Collect data during the detection of carbon content in the coal entering the furnace by the LIBS system in multiple groups in history; S2. Construct a mapping model of the detection error of the carbon content in the coal entering the furnace with the coal quality in combination with the data collected in S1; S3. Collect the parameter data of the coal entering the furnace and the corresponding number of LIBS detection probes during the current coal quality firing process in the coal-fired power plant and input them into the mapping model of the detection error of the carbon content in the coal entering the furnace with the coal quality for mapping, and then adjust the current initial number data of the LIBS detection probes in the coal entering the furnace according to the mapping result to obtain the current final number data of the LIBS detection probes in the coal entering the furnace; S4. Replace the coal quality on the current coal entering the furnace in combination with the data of the carbon content of the coal quality on the current coal entering the furnace detected with the current final number data of the LIBS detection probes in the coal entering the furnace and the corresponding electricity consumption scenario type to obtain the current replaced coal quality; S5. Adjust the combustion parameter data of various current types of coal quality in combination with the carbon content data of the current replaced coal quality to obtain the current final coal quality combustion parameter data set; Measure the carbon emissions after combustion in combination with the current final coal quality combustion parameter data set and the current replaced coal quality.

[0005] Preferably, S1 includes the following steps: S11. Set several parameter types related to the number of detection probes for detecting the carbon content in the coal entering the furnace by the LIBS system in the coal-fired power plant to obtain a set of parameter types affecting the carbon content detection in the coal entering the furnace; S12. In coordination with the set of influencing parameter types for the carbon content detection of coal entering the furnace, collect the number of detection probes of the LIBS system, the carbon content detection data, the actual carbon content data, and the influencing parameter data of the carbon content detection of the coal entering the furnace corresponding to multiple groups in history during the process of detecting the carbon content in the coal quality entering the furnace by using the LIBS system, so as to obtain the historical LIBS detection probe number set, the historical coal quality carbon content detection data set, the historical coal quality carbon content actual data set, and the historical influencing parameter data matrix of the carbon content detection of the coal entering the furnace; By setting the set of influencing parameter types for the carbon content detection of the coal entering the furnace, several parameters that have an impact on the data setting of the detection probes of the LIBS system when detecting the carbon content are determined; it provides a basis for collecting the corresponding influencing parameter data subsequently. By dynamically associating parameters such as the bandwidth and belt speed of the coal entering the furnace belt with the number of LIBS probes, the coal flow cross-sectional area and dynamic distribution characteristics can be accurately matched, and uniform sampling of the carbon content across the entire section can be achieved, which can cope with complex coal source scenarios; for example: when the bandwidth increases, the number of lateral probes is increased to avoid missing the detection of the carbon content at the edge of the coal flow; based on the dynamic matching of the conveying volume and the number of probes, the data update cycle can be shortened to within 12 minutes (traditional laboratory analysis takes more than 4 hours). In the scenario of high conveying volume (≥2000 t / h), through parallel acquisition by multiple probes, a full-section carbon content scan can be completed every minute; by parametrically configuring the number of probes, redundant deployment of fixed detection systems can be avoided.

[0006] Preferably, the S2 includes the following steps: S21. Calculate the relative error between the corresponding data in the historical coal quality carbon content detection data set and the historical coal quality carbon content actual data set to obtain the historical coal quality carbon content detection error data set; S22. Construct a mapping model for the coal quality carbon content detection error of the coal entering the furnace by using the historical LIBS detection probe number set, the historical coal quality carbon content detection error data set, and the historical influencing parameter data matrix of the carbon content detection of the coal entering the furnace; By constructing the mapping model for the coal quality carbon content detection error of the coal entering the furnace, a quantitative mapping relationship between the number of LIBS detection probes, various types of influencing parameter data of the carbon content detection of the coal entering the furnace, and the coal quality carbon content detection error of the coal entering the furnace is realized, which provides a mapping tool for subsequent quantitative adjustment of the number of LIBS detection probes, enabling the most suitable number of LIBS detection probes to be obtained more conveniently and accurately, and minimizing the coal quality carbon content detection error data of the coal entering the furnace.

[0007] Preferably, the S22 includes the following steps: S221. Construct the first initial SVM model and set the first training data ratio; divide the historical coal quality carbon content detection error data set, the historical LIBS detection probe number set, and the historical data matrix of the influence parameters of the coal carbon content detection into the furnace according to the first training data ratio to obtain the historical coal quality carbon content detection error training data set, the historical LIBS detection probe number training set, the historical data matrix of the influence parameters of the coal carbon content detection into the furnace training data matrix, the historical coal quality carbon content detection error test data set, the historical LIBS detection probe number test set, and the historical data matrix of the influence parameters of the coal carbon content detection into the furnace test data matrix; S222. Set the first training error threshold; input the historical LIBS detection probe number training set and the historical data matrix of the influence parameters of the coal carbon content detection into the furnace as training data and the historical coal quality carbon content detection error training data set as training labels into the first initial SVM model for training; during the training process, when the training error data is less than the first training error threshold, stop training to obtain the first trained SVM model; otherwise, continue training until the training error data is less than the first training error threshold; S223. Set the first test accuracy threshold; input the historical LIBS detection probe number test set and the historical data matrix of the influence parameters of the coal carbon content detection into the furnace as test data and the historical coal quality carbon content detection error test data set as test labels into the first trained SVM model for testing; after testing, obtain the first test accuracy data; when the first test accuracy data is greater than or equal to the first test accuracy threshold, use the first trained SVM model as the mapping model for the coal quality carbon content detection error in the coal entering the furnace; otherwise, return to S222 to continue training the first trained SVM model until the first test accuracy data is greater than or equal to the first test accuracy threshold; By inputting the collected historical data into the first initial SVM model for training and testing, the finally obtained mapping model for the coal quality carbon content detection error in the coal entering the furnace has the best mapping accuracy between the LIBS detection probe number, the influence parameters of the coal carbon content detection into the furnace, and the coal quality carbon content detection error test data; ensuring the accuracy of the data relied on when adjusting the LIBS detection probe number subsequently, and thus ensuring the accuracy of the adjustment of the LIBS detection probe number.

[0008] Preferably, the S3 includes the following steps: S31. In coordination with the set of influence parameter types for the carbon content detection of the coal entering the furnace, collect the data of the influence parameters for the carbon content detection of the coal entering the furnace during the current coal quality firing process in the coal-fired power plant and the number of LIBS detection probes set on the corresponding coal entering the furnace belt, so as to obtain the current dataset of influence parameters for the carbon content detection of the coal entering the furnace and the current data of the number of initial LIBS detection probes on the coal entering the furnace belt; S32. Set the current detection error threshold for the coal quality carbon content; input the current dataset of influence parameters for the carbon content detection of the coal entering the furnace and the current data of the number of initial LIBS detection probes on the coal entering the furnace belt into the mapping model of the detection error of the coal quality carbon content on the coal entering the furnace belt for mapping, so as to obtain the current detection error data of the coal quality carbon content; When the current detection error data of the coal quality carbon content is greater than or equal to the current detection error threshold of the coal quality carbon content, adjust the current data of the number of initial LIBS detection probes on the coal entering the furnace belt until the current detection error data of the coal quality carbon content is less than the current detection error threshold of the coal quality carbon content, and obtain the current final data of the number of LIBS detection probes on the coal entering the furnace belt; otherwise, there is no need to adjust the current data of the number of initial LIBS detection probes on the coal entering the furnace belt, and use the current data of the number of initial LIBS detection probes on the coal entering the furnace belt as the current final data of the number of LIBS detection probes on the coal entering the furnace belt; By adjusting the current data of the number of initial LIBS detection probes on the coal entering the furnace belt, the corresponding detection error data of the coal quality carbon content can meet the requirements; in addition, by collecting the real-time data of the coal quality carbon content on the coal entering the furnace belt, the estimation deviation of the carbon content caused by the coal quality fluctuation can be avoided, and the real-time carbon content data can be linked to the boiler control system to dynamically adjust parameters such as the air-coal ratio and combustion temperature: for example, when a sudden increase in the carbon content is detected, the secondary air volume is automatically increased to promote complete combustion, reduce the loss of unburned carbon and reduce the emission of harmful gases (carbon monoxide); at the same time, it also provides a data basis for optimizing the blending coal ratio in the subsequent firing process and reducing the carbon emission intensity per unit of power generation.

[0009] Preferably, in S32, the pelican optimization algorithm is used to adjust the current data of the number of initial LIBS detection probes on the coal entering the furnace belt.

[0010] Preferably, the S4 includes the following steps: S41. In coordination with the current final data of the number of LIBS detection probes on the coal entering the furnace belt, detect the carbon content of the coal quality on the current coal entering the furnace belt to obtain the current coal quality carbon content data; set the low-carbon content coal quality, medium-carbon content coal quality, and high-carbon content coal quality and their corresponding carbon content value ranges, so as to obtain the low-carbon content value range, medium-carbon content value range, and high-carbon content value range; Set several power consumption scenarios corresponding to low carbon content coal quality, medium carbon content coal quality, and high carbon content coal quality respectively, and obtain a low carbon content power consumption scenario set, a medium carbon content power consumption scenario set, and a high carbon content power consumption scenario set; S42. Obtain the current period power consumption scenario and set a coal quality replacement determination condition set; when there is a coal quality replacement determination condition in the coal quality replacement determination condition set that is satisfied, replace the coal quality on the current coal feeding belt until there is no coal quality replacement determination condition in the coal quality replacement determination condition set that is satisfied, obtain the current replaced coal quality, and enter S5; otherwise, there is no need to replace the coal quality on the current coal feeding belt; S43. Set parameters during the combustion of several types of coal quality to obtain a coal quality combustion parameter type set; collect the coal quality combustion parameter data, coal quality carbon content data, and corresponding coal quality combustion completeness data corresponding to the historical combustion of coal quality in cooperation with the coal quality combustion parameter type set, and obtain a historical coal quality combustion parameter data matrix, a historical coal quality carbon content data set, and a historical coal quality combustion completeness data set; construct a coal quality combustion completeness mapping model using the historical coal quality combustion parameter data matrix, the historical coal quality carbon content data set, and the historical coal quality combustion completeness data set; By dynamically updating the coal quality with different carbon contents in the furnace according to different power consumption scenarios, accurate control of carbon intensity is achieved, which is beneficial to reducing the total carbon emissions, being more environmentally friendly, and at the same time reducing the unit fuel consumption, thereby reducing the power generation cost; in addition, by constructing a historical coal quality combustion completeness mapping model, a quantitative mapping from coal quality combustion parameter data and coal quality carbon content data to coal quality combustion completeness data is achieved.

[0011] Preferably, S5 includes the following steps: S51. Obtain the carbon content data in the current replaced coal quality in S42 to obtain the current replaced coal quality carbon content data; S52. Adjust the current various types of coal quality combustion parameter data in cooperation with the current replaced coal quality carbon content data, the coal quality combustion completeness mapping model, and the coal quality combustion parameter type set to obtain the current final coal quality combustion parameter data set; S53. Burn the current replaced coal quality using the current final coal quality combustion parameter data set, and use a gas sensor to measure the carbon emissions after combustion in real time; By adjusting the current various types of coal quality combustion parameter data, it can be ensured that the coal quality carbon content in the current furnace can be fully burned in the burner. On the one hand, the coal quality fuel can be fully utilized, and on the other hand, the content of carbon monoxide gas caused by incomplete combustion is reduced, which is more environmentally friendly.

[0012] Preferably, the adjustment of the current coal quality combustion parameter data of various types in S52 includes the following steps: S521. Obtain the value range of the current coal quality combustion parameter data of various types in cooperation with the coal quality combustion parameter type set, and obtain the current coal quality combustion parameter value range set; Construct a pelican population for coal quality combustion parameter adjustment; set the maximum number of iterations of the pelican population for coal quality combustion parameter adjustment to and the current number of iterations to , which are the maximum number of iterations for combustion parameter adjustment and the current number of iterations for combustion parameter adjustment respectively; S522. Generate the initial position of each pelican in the pelican population for coal quality combustion parameter adjustment according to the current coal quality combustion parameter value range set, and obtain the second initial position matrix; S523. Construct the fitness function of the pelican population for coal quality combustion parameter adjustment; S524. Start iteration. Before iteration, set the current number of iterations for combustion parameter adjustment to 1; in the first round of iteration, calculate the fitness value of the initial position of each pelican in the second initial position matrix by using the fitness function of the pelican population for coal quality combustion parameter adjustment, and obtain the third fitness value set; take the maximum fitness value in the third fitness value set and the corresponding initial position of the pelican as the third global best fitness and the third global best position respectively; update the initial position of each pelican in the second initial position matrix according to the third global best fitness and the third global best position; after the update is completed, add 1 to the current number of iterations for combustion parameter adjustment and perform the next round of iteration; In each other round of iteration, calculate the fitness value of the position of each pelican in the pelican population for coal quality combustion parameter adjustment updated in the previous round of iteration by using the fitness function of the pelican population for coal quality combustion parameter adjustment, and obtain the fourth fitness value set; take the maximum fitness value in the fourth fitness value set and the corresponding position of the pelican as the fourth global best fitness and the fourth global best position respectively; update the position of each pelican in the pelican population for coal quality combustion parameter adjustment updated in the previous round of iteration according to the fourth global best fitness and the fourth global best position; after the update is completed, add 1 to the current number of iterations for combustion parameter adjustment and perform the next round of iteration; S525. When , stop iteration and obtain the second final global best position; otherwise, continue iteration until ; take the second final global best position as the current final coal quality combustion parameter data set; The Pelican optimization algorithm can efficiently search the multi-dimensional parameter space by simulating the group predation behavior, avoiding the defect of the traditional gradient descent method that is prone to falling into local optima. For key parameters such as the burner outlet size, the recirculation zone intensity of the swirl burner, the boiler load, and the oxygen concentration, the algorithm can find the global optimal solution within 20 - 50 iterations, reducing the carbon content in fly ash to less than 2%. Moreover, it supports real-time response to changes in combustion conditions (such as load fluctuations and coal quality fluctuations), and quickly matches the optimal parameter combination by adaptively adjusting the position update strategy.

[0013] A real-time monitoring system for carbon emission indicators in coal-fired power plants includes a parameter type setting module for the number of carbon content detection probes, a historical carbon content detection process data acquisition module, a carbon content detection error mapping model construction module, a current coal quality data acquisition module for coal entering the furnace, a current LIBS detection probe number adjustment module, a current coal quality update module for coal entering the furnace, a current coal quality combustion parameter adjustment module, and a final carbon emission measurement module.

[0014] The present invention has the following beneficial effects: 1. In the present invention, by first optimizing the detection accuracy of the carbon content of the coal quality before entering the furnace based on adjusting the number of LIBS detection probes set on the coal quality belt for coal entering the furnace, it is ensured that the detection accuracy of the carbon content of the coal quality before entering the furnace meets the requirements. Then, according to the detected carbon content data of the coal quality before entering the furnace and combined with the corresponding power consumption scenarios, the coal quality before entering the furnace is replaced, making the carbon content of the coal quality before entering the furnace more in line with the current power consumption scenario, which is conducive to saving fuel and improving power generation efficiency. Finally, by adjusting the combustion parameters of the corresponding burner according to the carbon content data of the replaced coal quality before entering the furnace, it is ensured that the coal quality is fully burned, which not only makes full use of the fuel but also reduces the proportion of harmful gases during final carbon emissions.

[0015] 2. In the present invention, by collecting real-time data on the carbon content of the coal quality on the coal belt for coal entering the furnace, it is possible to avoid the deviation in carbon content estimation caused by coal quality fluctuations. The real-time carbon content data can be linked to the boiler control system to dynamically adjust parameters such as the air-coal ratio and combustion temperature. For example, when a sudden increase in carbon content is detected, the secondary air volume is automatically increased to promote complete combustion, reduce unburned carbon loss, and reduce the emission of harmful gases (carbon monoxide). At the same time, it also provides a data basis for optimizing the blending coal ratio in the subsequent firing process and reducing the carbon emission intensity per unit of power generation.

[0016] 3. In the present invention, by dynamically updating the coal quality with different carbon contents entering the furnace according to different power consumption scenarios, precise control of carbon intensity is achieved, which is conducive to reducing the total carbon emissions, being more environmentally friendly, and at the same time reducing the unit fuel consumption, thereby reducing the power generation cost.

[0017] Of course, it is not necessary for any product implementing the present invention to simultaneously achieve all the above-mentioned advantages. Brief Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a schematic flowchart of a method for real-time monitoring of carbon emission indicators in a coal-fired power plant according to the present invention; Figure 2 It is a schematic flowchart of adjusting the number of detection probes of the LIBS system according to the present invention; Figure 3 It is a schematic flowchart of adjusting the combustion parameter data of various types of coal quality at present according to the present invention; Figure 4 It is a schematic diagram of the modules of a real-time monitoring system for carbon emission indicators in a coal-fired power plant according to the present invention. Detailed Embodiments

[0020] The technical solutions in the embodiments of the invention will be clearly and completely described below with reference to the drawings in the embodiments of the invention. Obviously, the described embodiments are only some, rather than all, of the embodiments of the invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the invention without creative efforts fall within the scope of protection of the invention.

[0021] Embodiment 1 Please refer to Figures 1-3 , this embodiment is a method for real-time monitoring of carbon emission indicators in a coal-fired power plant, including the following steps: S1. Collect data during the detection of the carbon content in the coal fed into the furnace by the LIBS system in multiple groups in history; The S1 includes the following steps: S11. Set several parameter types associated with the number of detection probes for detecting the carbon content of the coal entering the furnace in a coal-fired power plant using a LIBS system, and obtain a set of parameter types affecting the detection of carbon content in the coal entering the furnace; the set of parameter types affecting the detection of carbon content in the coal entering the furnace includes parameter types of furnace bandwidth, furnace speed, conveying volume, and conveying length, etc.; the larger the bandwidth, the larger the cross-sectional area of the coal flow, and the more probes are needed to ensure coverage of carbon content detection across the entire width of the coal seam. A large bandwidth can easily lead to differences in the accumulation of the coal flow edge and center (e.g., when the bandwidth is ≥1400mm), and additional probes need to be installed on both sides to monitor stratification. A high belt speed (e.g., >3m / s) requires an increased sampling frequency of the probes. , high-frequency data collection can be achieved by increasing the number of parallel probes. Too fast belt speed (>4m / s) is likely to cause coal flow fluctuations, and probes need to be added in the middle of the conveying section to capture instantaneous carbon content changes; long-distance transportation (L>100m) is likely to cause coal flow stratification or segregation, and probes need to be set up in sections (such as 1 group every 50m) to monitor carbon content gradient changes. Additional probes need to be added at the head (discharging end) and tail (receiving end) of the conveyor to eliminate detection deviations caused by material impact; high conveying volume corresponds to thicker coal seams, and probes need to be arranged in layers in the vertical direction (such as upper and lower double probes) to measure cross-sectional carbon distribution; when Q≥2000t / h, probes need to be evenly distributed on both sides of the belt and the center line (such as 3-5 / section) to avoid local detection failures due to coal flow accumulation; S12, in conjunction with the coal feeding zone carbon content detection influencing parameter type set, collect the number of detection probes of the LIBS system, carbon content detection data, actual carbon content data, and carbon content detection influencing parameter data of the corresponding coal feeding zone during the historical process of using the LIBS system to detect the carbon content in the coal quality entering the furnace, and obtain the historical LIBS detection probe number set , Historical coal quality carbon content detection dataset , Actual dataset of historical coal carbon content and the historical coal feeding carbon content detection influencing parameter data matrix; a1i, a2i, and a3i represent the number of LIBS system detection probes, carbon content detection data, and actual carbon content data corresponding to the i-th group of historical LIBS system detection of carbon content in the feeding coal. It represents the total number of groups collected during the process of testing the carbon content of the coal entering the furnace using the LIBS system; S2, build a coal quality carbon content detection error mapping model in the coal entering the furnace with the data collected in S1; The S2 comprises the following steps: S21, calculate the relative error between the historical coal quality carbon content detection data set and the corresponding data in the historical coal quality carbon content actual data set, and obtain the historical coal quality carbon content detection error data set , where \(a_{4i}\) represents the error between \(a_{2i}\) and \(a_{3i}\); the calculation formula is as follows, ; S22. Construct a detection error mapping model of the coal quality carbon content in the coal entering the furnace by using the historical LIBS detection probe number set, the historical detection error data set of the coal quality carbon content, and the historical data matrix of the influence parameters of the coal carbon content detection when entering the furnace; The S22 includes the following steps: S221. Construct a first initial SVM model and set a first training data ratio; divide the historical detection error data set of the coal quality carbon content, the historical LIBS detection probe number set, and the historical data matrix of the influence parameters of the coal carbon content detection when entering the furnace according to the first training data ratio to obtain a historical training data set of the detection error of the coal quality carbon content, a historical training set of the LIBS detection probe number, a historical training data matrix of the influence parameters of the coal carbon content detection when entering the furnace, a historical test data set of the detection error of the coal quality carbon content, a historical test set of the LIBS detection probe number, and a historical test data matrix of the influence parameters of the coal carbon content detection when entering the furnace; S222. Set a first training error threshold; input the historical training set of the LIBS detection probe number, the historical training data matrix of the influence parameters of the coal carbon content detection when entering the furnace as training data and the historical training data set of the detection error of the coal quality carbon content as training labels into the first initial SVM model for training; during the training process, when the training error data is less than the first training error threshold, stop training to obtain a first trained SVM model; otherwise, continue training until the training error data is less than the first training error threshold; S223. Set a first test accuracy threshold; input the historical test set of the LIBS detection probe number, the historical test data matrix of the influence parameters of the coal carbon content detection when entering the furnace as test data and the historical test data set of the detection error of the coal quality carbon content as test labels into the first trained SVM model for testing; after the test is completed, obtain the first test accuracy data; when the first test accuracy data is greater than or equal to the first test accuracy threshold, use the first trained SVM model as the detection error mapping model of the coal quality carbon content in the coal entering the furnace; otherwise, return to S222 to continue training the first trained SVM model until the first test accuracy data is greater than or equal to the first test accuracy threshold; S3. Collect the parameter data of the coal entering the furnace belt and the corresponding number of LIBS detection probes during the current coal quality firing process in the coal-fired power plant, and input them into the coal quality carbon content detection error mapping model of the coal entering the furnace belt for mapping. Then, adjust the current initial number data of the LIBS detection probes on the coal entering the furnace belt according to the mapping result to obtain the current final number data of the LIBS detection probes on the coal entering the furnace belt; S3 includes the following steps: S31. Cooperate with the set of carbon content detection influence parameter types of the coal entering the furnace belt to collect the carbon content detection influence parameter data of the coal entering the furnace belt and the number of LIBS detection probes set on the corresponding coal entering the furnace belt during the current coal quality firing process in the coal-fired power plant, so as to obtain the current carbon content detection influence parameter data set of the coal entering the furnace belt and the current initial number data of the LIBS detection probes on the coal entering the furnace belt; S32. Set the current coal quality carbon content detection error threshold; input the current carbon content detection influence parameter data set of the coal entering the furnace belt and the current initial number data of the LIBS detection probes on the coal entering the furnace belt into the coal quality carbon content detection error mapping model of the coal entering the furnace belt for mapping to obtain the current coal quality carbon content detection error data; If the current coal quality carbon content detection error data is greater than or equal to the current coal quality carbon content detection error threshold, adjust the current initial number data of the LIBS detection probes on the coal entering the furnace belt until the current coal quality carbon content detection error data is less than the current coal quality carbon content detection error threshold, and obtain the current final number data of the LIBS detection probes on the coal entering the furnace belt; otherwise, there is no need to adjust the current initial number data of the LIBS detection probes on the coal entering the furnace belt, and use the current initial number data of the LIBS detection probes on the coal entering the furnace belt as the current final number data of the LIBS detection probes on the coal entering the furnace belt; The adjustment of the current initial number data of the LIBS detection probes on the coal entering the furnace belt in S32 includes the following steps: S321. Set the value range of the current initial number data of the LIBS detection probes on the coal entering the furnace belt to obtain the current LIBS detection probe number value range ; 、 respectively represent the lower limit and the upper limit of the value of the current initial number data of the LIBS detection probes on the coal entering the furnace belt; Construct a pelican population for adjusting the number of LIBS detection probes on the coal entering the furnace belt; set the maximum number of iterations of the pelican population for adjusting the number of LIBS detection probes on the coal entering the furnace belt as and the current number of iterations as , which are respectively recorded as the maximum number of iterations for probe adjustment and the current number of iterations for probe adjustment; the search space dimension of the pelican population for adjusting the number of LIBS detection probes on the coal entering the furnace belt is 1 dimension; S322. Generate the initial position of each pelican in the pelican population for adjusting the number of LIBS detection probes in the coal entering the furnace according to the value range of the current number of LIBS detection probes, and obtain the first initial position set. , where b1i represents the initial position of the i-th pelican in the pelican population for adjusting the number of LIBS detection probes in the coal entering the furnace. represents the scale of the pelican population for adjusting the number of LIBS detection probes in the coal entering the furnace; the generation formula of b1i is as follows. ; In the formula: rand1i represents a random number between 0 and 1 generated for b1i; ceil represents the rounding function. S323. Construct the fitness function of the pelican population for adjusting the number of LIBS detection probes in the coal entering the furnace. ; as follows. ; In the formula, represents mapping the number of LIBS detection probes in the coal entering the furnace obtained in each iteration process and the current data set of influence parameters for detecting the carbon content in the coal entering the furnace into the coal quality carbon content detection error mapping model in the coal entering the furnace to obtain mapping data. is a positive number, representing the protection parameter. S324. Start the iteration. Before the iteration, set the current iteration number of the probe adjustment to 1; in the first round of iteration, use the fitness function of the pelican population for adjusting the number of LIBS detection probes in the coal entering the furnace. Calculate the fitness value of the initial position of each pelican in the first initial position set, and obtain the first fitness value set; take the maximum fitness value in the first fitness value set and the corresponding initial position of the pelican as the first global best fitness and the first global best position respectively; update the initial position of each pelican in the first initial position set according to the first global best fitness and the first global best position; after the update is completed, add 1 to the current iteration number of the probe adjustment and perform the next round of iteration. In each subsequent round of iteration, use the fitness function of the pelican population for adjusting the number of LIBS detection probes in the coal entering the furnace. Calculate the fitness value of each pelican's position in the pelican population adjusted by the number of LIBS detection probes for coal entering the furnace in the previous iteration process to obtain the second fitness value set; use the maximum fitness value in the second fitness value set and the corresponding pelican position as the second global best fitness and the second global best position respectively; update the positions of each pelican in the pelican population adjusted by the number of LIBS detection probes for coal entering the furnace in the previous iteration process according to the second global best fitness and the second global best position; after the update is completed, increment the current iteration count of the probe adjustment by 1 and perform the next iteration; S325. When occurs, stop the iteration to obtain the first final global best position and the first final global best fitness; otherwise, continue the iteration until occurs; use the first final global best fitness as the current optimized coal quality carbon content detection error data; when the current optimized coal quality carbon content detection error data is less than the current coal quality carbon content detection error threshold, use the first final global best position as the current final number of LIBS detection probes for coal entering the furnace data; otherwise, return to S324 to continue the iteration until the current optimized coal quality carbon content detection error data is less than the current coal quality carbon content detection error threshold; The pelican optimization algorithm supports real-time data-driven updates. Combining with LIBS spectral stability parameters (such as the relative standard deviation RSD between pulses), it dynamically adjusts the number of activated probes: for example, when the uniformity of the pulverized coal particle flow decreases (RSD≥8%), it automatically increases the number of effective probes to improve data redundancy and ensure detection reliability; in the stage of stable coal quality (RSD≤3%), only the core probe group is enabled to extend the equipment life and reduce energy consumption; optimizing the number and layout of LIBS probes through the pelican optimization algorithm can improve the global efficiency of the detection-optimization-control link in coal-fired power plants while ensuring the accuracy of carbon emission accounting; S4. Replace the coal quality on the current coal entering the furnace with the detected carbon content data of the coal quality on the current coal entering the furnace and the corresponding power consumption scenario type according to the current final number of LIBS detection probes for coal entering the furnace data to obtain the current replaced coal quality; S4 includes the following steps: S41. Detect the carbon content of the coal quality on the current coal entering the furnace according to the current final number of LIBS detection probes for coal entering the furnace data to obtain the current coal quality carbon content data; set low-carbon-content coal quality, medium-carbon-content coal quality, and high-carbon-content coal quality and their corresponding carbon content value ranges to obtain the low-carbon-content value range, medium-carbon-content value range, and high-carbon-content value range; Set several power consumption scenarios corresponding to low-carbon coal quality, medium-carbon coal quality, and high-carbon coal quality respectively, to obtain a low-carbon power consumption scenario set, a medium-carbon power consumption scenario set, and a high-carbon power consumption scenario set; for example, during the peak power consumption period, high-carbon and high-calorific value coal is preferentially co-fired (such as the proportion of Malang coal is 65%), which can improve the instantaneous output of the boiler and reduce the loss of unburned carbon; during the low power consumption period, low-price and low-calorific value coal is co-fired (carbon content ≤ 50%), and high-carbon and high-calorific value coal is used during the peak period, which can shorten the combustion time, reduce the loss of unburned carbon, and reduce the carbon emission intensity per unit of electricity generation; during the low period, low-carbon coal is co-fired (carbon content ≤ 50%) to directly reduce the total carbon emission; through dynamic matching of coal quality, the emission concentration of sulfur oxides (SOx) can be controlled below 50mg / m, meeting the ultra-low emission standard; high-carbon coal has a lower ash content (Aar ≤ 20%), which can reduce the ash slag generation by 30%-40% and reduce the carbon content of fly ash to below 2%; low-carbon coal has a higher ash melting point (ST ≥ 1350°C), which can inhibit the risk of coking; S42. Obtain the power consumption scenario of the current period and set a set of coal quality replacement determination conditions , where c1 means: when the current coal quality carbon content data is within the low-carbon content value range and the power consumption scenario of the current period is not in the low-carbon power consumption scenario set; c2 means: when the current coal quality carbon content data is within the medium-carbon content value range and the power consumption scenario of the current period is not in the medium-carbon power consumption scenario set; c3 means: when the current coal quality carbon content data is within the high-carbon content value range and the power consumption scenario of the current period is not in the high-carbon power consumption scenario set; When there is a coal quality replacement determination condition in the set of coal quality replacement determination conditions that is satisfied, replace the coal quality on the current coal feeding belt until there is no coal quality replacement determination condition in the set of coal quality replacement determination conditions that is satisfied, to obtain the current replaced coal quality and enter S5; otherwise, there is no need to replace the coal quality on the current coal feeding belt; S43. Set parameters during the combustion process of several types of coal quality to obtain a set of coal quality combustion parameter types; the set of coal quality combustion parameter types includes the burner outlet size. For example, when the outlet diameter > 800mm, it will cause the ignition distance of the pulverized coal air flow to extend by 2-3 meters, increasing the loss of unburned carbon; the recirculation zone intensity of the swirl burner. For example, when the recirculation zone intensity of the swirl burner is insufficient, the entrainment amount of high-temperature flue gas decreases and the ignition stability decreases; the boiler load and oxygen concentration. When the load rate < 60%, the average furnace temperature drops by 200-300°C, and the carbon content of fly ash increases by 3-5 percentage points. When the flue gas oxygen concentration < 3%, the oxygen supply is insufficient in the later stage of combustion, and an oxygen-deficient protective layer is formed on the surface of large-particle pulverized coal, etc.; collect the coal quality combustion parameter data, coal quality carbon content data, and corresponding coal quality combustion completeness data corresponding to the combustion of coal quality in history in combination with the set of coal quality combustion parameter types, to obtain a historical coal quality combustion parameter data matrix and a historical coal quality carbon content data set and the historical coal quality combustion completeness data set , 、 respectively represent the carbon content data and the coal quality combustion completeness data of the i-th group of collected historical coal quality during combustion, represents the total number of groups of data during the combustion process of the collected historical coal quality; Construct a coal quality combustion completeness mapping model by using the historical coal quality combustion parameter data matrix, the historical coal quality carbon content data set, and the historical coal quality combustion completeness data set; Preferably, constructing the coal quality combustion completeness mapping model by using the historical coal quality combustion parameter data matrix, the historical coal quality carbon content data set, and the historical coal quality combustion completeness data set in S43 includes the following steps: S431. Construct a second initial SVM model and set a second training data ratio; divide the historical coal quality combustion parameter data matrix, the historical coal quality carbon content data set, and the historical coal quality combustion completeness data set according to the second training data ratio to obtain a historical coal quality combustion parameter training data matrix, a historical coal quality carbon content training data set, a historical coal quality combustion completeness training data set, a historical coal quality combustion parameter test data matrix, a historical coal quality carbon content test data set, and a historical coal quality combustion completeness test data set; S432. Set a second training error threshold; input the historical coal quality combustion parameter training data matrix and the historical coal quality carbon content training data set as training data and the historical coal quality combustion completeness training data set as training labels into the second initial SVM model for training; during the training process, when the training error data is less than the second training error threshold, stop training to obtain a second trained SVM model; otherwise, continue training until the training error data is less than the second training error threshold; S433. Set a second test accuracy threshold; input the historical coal quality combustion parameter test data matrix and the historical coal quality carbon content test data set as test data and the historical coal quality combustion completeness test data set as test labels into the second trained SVM model for testing; after testing, obtain the second test accuracy data; when the second test accuracy data is greater than or equal to the second test accuracy threshold, use the second trained SVM model as the coal quality combustion completeness mapping model; otherwise, return to S432 to continue training the second trained SVM model until the second test accuracy data is greater than or equal to the second test accuracy threshold; S5. Adjust the current various types of coal quality combustion parameter data in combination with the carbon content data of the currently replaced coal quality to obtain the current final coal quality combustion parameter data set; Measure the carbon emissions after combustion in combination with the current final coal quality combustion parameter dataset and the current replaced coal quality; Step S5 includes the following steps: S51. Obtain the carbon content data in the current replaced coal quality in S42 to obtain the current replaced coal quality carbon content data; S52. Adjust the current various types of coal quality combustion parameter data in combination with the current replaced coal quality carbon content data, the coal quality combustion completeness mapping model, and the coal quality combustion parameter type set to obtain the current final coal quality combustion parameter dataset; The adjustment of the current various types of coal quality combustion parameter data in S52 includes the following steps: S521. Obtain the value range of the current various types of coal quality combustion parameter data in combination with the coal quality combustion parameter type set to obtain the current coal quality combustion parameter value range set ; as follows, ; where and respectively represent the lower limit and upper limit of the value of the current i-th type of coal quality combustion parameter data, and d represents the total number of set coal quality combustion parameter types; Construct a coal quality combustion parameter adjustment pelican population; set the maximum number of iterations of the coal quality combustion parameter adjustment pelican population to and the current number of iterations to , which are the maximum number of iterations for combustion parameter adjustment and the current number of iterations for combustion parameter adjustment respectively; the number of search space dimensions of the coal quality combustion parameter adjustment pelican population is the same as d; S522. Generate the initial position of each pelican in the coal quality combustion parameter adjustment pelican population according to the current coal quality combustion parameter value range set to obtain a second initial position matrix; the calculation formula is as follows, ; where represents the position component of the initial position of the j-th pelican in the coal quality combustion parameter adjustment pelican population in the dimension of the i-th type of coal quality combustion parameter data; represents a random number generated for between 0 and 1; S523. Construct the fitness function of the coal quality combustion parameter adjustment pelican population ; as follows, ; In the formula, It represents the data obtained by inputting a set of coal combustion parameters updated in each iteration process and the current carbon content data of the replaced coal quality into the coal combustion completeness mapping model for mapping. S524. Start the iteration. Before the iteration, set the current iteration number of the combustion parameter adjustment to 1. In the first round of the iteration process, use the coal combustion parameters to adjust the fitness function of the pelican population. Calculate the fitness values of the initial positions of each pelican in the second initial position matrix to obtain the third fitness value set. Take the maximum fitness value in the third fitness value set and the corresponding initial position of the pelican as the third global best fitness and the third global best position respectively. Update the initial positions of each pelican in the second initial position matrix according to the third global best fitness and the third global best position. After the update is completed, increment the current iteration number of the combustion parameter adjustment by 1 and perform the next round of iteration. In each subsequent round of the iteration process, use the coal combustion parameters to adjust the fitness function of the pelican population. Calculate the fitness values of the positions of each pelican in the pelican population adjusted by the coal combustion parameters updated in the previous round of the iteration process to obtain the fourth fitness value set. Take the maximum fitness value in the fourth fitness value set and the corresponding position of the pelican as the fourth global best fitness and the fourth global best position respectively. Update the positions of each pelican in the pelican population adjusted by the coal combustion parameters updated in the previous round of the iteration process according to the fourth global best fitness and the fourth global best position. After the update is completed, increment the current iteration number of the combustion parameter adjustment by 1 and perform the next round of iteration. S525. When holds, stop the iteration to obtain the second final global best position; otherwise, continue the iteration until holds. Take the second final global best position as the current final coal combustion parameter data set. S53. Burn the current replaced coal quality using the current final coal combustion parameter data set, and use a gas sensor to measure the carbon emissions after combustion in real time.

[0022] Embodiment 2 Please refer to Figure 4 , this embodiment discloses a real-time monitoring system for carbon emission indicators of a coal-fired power plant. The system can implement the method of the above embodiment, including a parameter type setting module for the number of carbon content detection probes, a historical carbon content detection process data acquisition module, a carbon content detection error mapping model construction module, a current coal quality furnace inlet data acquisition module, a current LIBS detection probe number adjustment module, a current furnace inlet coal quality update module, a current coal combustion parameter adjustment module, and a final carbon emission measurement module. The carbon content detection probe quantity influence parameter type setting module sets several parameter types associated with the quantity of detection probes for detecting the carbon content in the coal fed into the furnace using the LIBS system, and obtains the carbon content detection influence parameter type set for the coal fed into the furnace; The historical carbon content detection process data acquisition module cooperates with the carbon content detection influence parameter type set for the coal fed into the furnace to collect data during the historical detection of the carbon content in the coal fed into the furnace using the LIBS system for multiple groups, and obtains the historical LIBS detection probe quantity set, the historical coal quality carbon content detection data set, the historical coal quality carbon content actual data set, and the historical carbon content detection influence parameter data matrix for the coal fed into the furnace; The carbon content detection error mapping model construction module constructs a carbon content detection error mapping model for the coal quality in the coal fed into the furnace by cooperating with the error between the historical LIBS detection probe quantity set, the historical carbon content detection influence parameter data matrix for the coal fed into the furnace, the historical coal quality carbon content detection data set, and the historical coal quality carbon content actual data set; The current coal quality fed into the furnace data acquisition module cooperates with the carbon content detection influence parameter type set for the coal fed into the furnace to collect the parameter data of the coal fed into the furnace and the corresponding LIBS detection probe quantity during the current coal quality firing process in the coal-fired power plant, and obtains the current carbon content detection influence parameter data set for the coal fed into the furnace and the current initial LIBS detection probe quantity data for the coal fed into the furnace; The current LIBS detection probe quantity adjustment module maps the current carbon content detection influence parameter data set for the coal fed into the furnace and the current initial LIBS detection probe quantity data for the coal fed into the furnace into the carbon content detection error mapping model for the coal quality in the coal fed into the furnace, and then adjusts the current initial LIBS detection probe quantity data for the coal fed into the furnace according to the mapping result to obtain the current final LIBS detection probe quantity data for the coal fed into the furnace; The current coal quality fed into the furnace update module cooperates with the current final LIBS detection probe quantity data for the coal fed into the furnace to detect the carbon content of the coal quality on the current coal fed into the furnace and the corresponding power consumption scenario type to replace the coal quality on the current coal fed into the furnace, and obtains the current replaced coal quality; The current coal quality combustion parameter adjustment module adjusts the current various types of coal quality combustion parameter data according to the carbon content data of the current replaced coal quality to obtain the current final coal quality combustion parameter data set; The final carbon emission measurement module measures the carbon emissions after combustion in cooperation with the current final coal quality combustion parameter data set and the current replaced coal quality.

[0023] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0024] The preferred embodiments of the invention disclosed above are only used to help illustrate the invention. The preferred embodiments do not exhaust all the details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the invention, so that those skilled in the art can well understand and utilize the invention.

Claims

1. A real-time monitoring method for carbon emission indicators of coal-fired power plants, characterized in that It includes the following steps: S1. Collect data during the detection of carbon content in the coal entering the furnace by the LIBS system in multiple historical groups; S2. Construct a detection error mapping model for the carbon content of the coal entering the furnace in combination with the data collected in S1; S3. Collect the parameter data of the coal entering the furnace and the corresponding number of LIBS detection probes during the current coal quality firing process in the coal-fired power plant and input them into the detection error mapping model for the carbon content of the coal entering the furnace to perform mapping, and then adjust the current initial number of LIBS detection probe data for the coal entering the furnace according to the mapping result to obtain the current final number of LIBS detection probe data for the coal entering the furnace; S4. Replace the coal quality on the current coal entering the furnace in combination with the data of the carbon content of the coal quality detected on the current coal entering the furnace corresponding to the current final number of LIBS detection probes and the corresponding power consumption scenario type to obtain the current replaced coal quality; S5. Adjust the combustion parameter data of various current coal qualities in combination with the carbon content data of the current replaced coal quality to obtain the current final coal quality combustion parameter data set; Measure the carbon emissions after combustion in combination with the current final coal quality combustion parameter data set and the current replaced coal quality.

2. The real-time monitoring method for carbon emission index of a coal-fired power plant according to claim 1, characterized in that The S1 includes the following steps: S11. Set several parameter types associated with the number of detection probes for detecting the carbon content in the coal entering the furnace by the LIBS system in the coal-fired power plant to obtain a set of influencing parameter types for the carbon content detection of the coal entering the furnace; S12. In combination with the set of influencing parameter types for the carbon content detection of the coal entering the furnace, collect the number of detection probes of the LIBS system, the carbon content detection data, the actual carbon content data, and the influencing parameter data for the carbon content detection of the corresponding coal entering the furnace during the detection of the carbon content in the coal entering the furnace by the LIBS system in multiple historical groups to obtain a historical LIBS detection probe number set, a historical coal quality carbon content detection data set, a historical coal quality carbon content actual data set, and a historical coal entering the furnace carbon content detection influencing parameter data matrix.

3. A real-time monitoring method for carbon emission indicators of a coal-fired power plant according to claim 2, characterized in that, The S2 includes the following steps: S21. Calculate the relative error between the corresponding data in the historical coal quality carbon content detection data set and the historical coal quality carbon content actual data set to obtain a historical coal quality carbon content detection error data set; S22. Construct a detection error mapping model for the carbon content of the coal entering the furnace using the historical LIBS detection probe number set, the historical coal quality carbon content detection error data set, and the historical coal entering the furnace carbon content detection influencing parameter data matrix.

4. A real-time monitoring method for carbon emission indicators of a coal-fired power plant according to claim 3, characterized in that The detection error mapping model for the carbon content of the coal entering the furnace in S22 is constructed based on the SVM model.

5. A real-time monitoring method for carbon emission indicators of a coal-fired power plant according to claim 4, characterized in that The S3 includes the following steps: S31. In combination with the set of influencing parameter types for the carbon content detection of the coal entering the furnace, collect the influencing parameter data for the carbon content detection of the coal entering the furnace and the number of LIBS detection probes set on the corresponding coal entering the furnace during the current coal quality firing process in the coal-fired power plant to obtain a current coal entering the furnace carbon content detection influencing parameter data set and the current initial number of LIBS detection probe data for the coal entering the furnace; S32. Set the current detection error threshold for the carbon content of coal quality; input the current dataset of influencing parameters for detecting the carbon content of coal entering the furnace and the current number of LIBS detection probes for the initial coal entering the furnace into the mapping model of the detection error of the carbon content of coal entering the furnace for mapping to obtain the current detection error data of the carbon content of coal quality; If the current detection error data of the carbon content of coal quality is greater than or equal to the current detection error threshold for the carbon content of coal quality, adjust the current number of LIBS detection probes for the initial coal entering the furnace until the current detection error data of the carbon content of coal quality is less than the current detection error threshold for the carbon content of coal quality, and obtain the current final number of LIBS detection probes for the coal entering the furnace; otherwise, there is no need to adjust the current number of LIBS detection probes for the initial coal entering the furnace, and use the current number of LIBS detection probes for the initial coal entering the furnace as the current final number of LIBS detection probes for the coal entering the furnace.

6. The real-time monitoring method for carbon emission index of a coal-fired power plant according to claim 5, characterized in that: In S32, the pelican optimization algorithm is used to adjust the current number of LIBS detection probes for the initial coal entering the furnace.

7. A real-time monitoring method for carbon emission indicators of a coal-fired power plant according to claim 6, characterized in that The S4 includes the following steps: S41. Detect the carbon content of the coal quality on the current coal entering the furnace in cooperation with the current final number of LIBS detection probes for the coal entering the furnace to obtain the current carbon content data of the coal quality; set the low-carbon-content coal quality, medium-carbon-content coal quality, and high-carbon-content coal quality and their corresponding carbon content value ranges to obtain the low-carbon-content value range, medium-carbon-content value range, and high-carbon-content value range; respectively set several types of power consumption scenarios corresponding to the low-carbon-content coal quality, medium-carbon-content coal quality, and high-carbon-content coal quality to obtain the low-carbon-content power consumption scenario set, medium-carbon-content power consumption scenario set, and high-carbon-content power consumption scenario set; S42. Obtain the current power consumption scenario and set the coal quality replacement determination condition set; when there is a coal quality replacement determination condition in the coal quality replacement determination condition set that is satisfied, replace the coal quality on the current coal entering the furnace until there is no coal quality replacement determination condition in the coal quality replacement determination condition set that is satisfied, and obtain the current replaced coal quality and enter S5; otherwise, there is no need to replace the coal quality on the current coal entering the furnace; S43. Set the parameters during the combustion process of several types of coal quality to obtain the set of coal quality combustion parameter types; collect the corresponding coal quality combustion parameter data, coal quality carbon content data, and corresponding coal quality combustion completeness data during the historical combustion of coal quality in cooperation with the set of coal quality combustion parameter types to obtain the historical coal quality combustion parameter data matrix, historical coal quality carbon content dataset, and historical coal quality combustion completeness dataset; Construct a mapping model of coal quality combustion completeness using the historical coal quality combustion parameter data matrix, historical coal quality carbon content dataset, and historical coal quality combustion completeness dataset.

8. A real-time monitoring method for carbon emission indicators of a coal-fired power plant according to claim 7, characterized in that, The S5 includes the following steps: S51. Obtain the carbon content data in the current replaced coal quality in S42 to obtain the current carbon content data of the replaced coal quality; S52. Adjust the current coal quality combustion parameter data of various types according to the current coal quality carbon content data after replacement, the coal quality combustion completeness mapping model, and the coal quality combustion parameter type set to obtain the current final coal quality combustion parameter data set; S53. Burn the current coal quality after replacement using the current final coal quality combustion parameter data set, and use a gas sensor to measure the carbon emissions after combustion in real time.

9. A real-time monitoring method for carbon emission index of a coal-fired power plant according to claim 8, characterized in that, The adjustment of the current coal quality combustion parameter data of various types in S52 includes the following steps: S521. Obtain the value range of the current coal quality combustion parameter data for each type in accordance with the coal quality combustion parameter type set, and obtain the current coal quality combustion parameter value range set; construct a pelican population for coal quality combustion parameter adjustment; set the maximum number of iterations of the pelican population for coal quality combustion parameter adjustment as and the current number of iterations as , which are respectively the maximum number of iterations for combustion parameter adjustment and the current number of iterations for combustion parameter adjustment; S522. Generate the initial position of each pelican in the coal quality combustion parameter adjustment pelican population according to the current coal quality combustion parameter value range set to obtain the second initial position matrix; S523. Construct the fitness function of the coal quality combustion parameter adjustment pelican population; S524. Start the iteration. Before the iteration, set the current iteration number of the combustion parameter adjustment to 1; in each round of iteration, calculate the fitness value of the position of each pelican in the coal quality combustion parameter adjustment pelican population updated in the previous round of iteration using the fitness function of the coal quality combustion parameter adjustment pelican population and update the position of each pelican in the coal quality combustion parameter adjustment pelican population updated in the previous round of iteration; after the update is completed, add 1 to the current iteration number of the combustion parameter adjustment and perform the next round of iteration; S525. When , stop the iteration to obtain the second final global optimal position; otherwise, continue the iteration until ; use the second final global optimal position as the current final coal quality combustion parameter data set.

10. A system for implementing the real-time monitoring method of the carbon emission index of a coal-fired power plant according to any one of claims 1-9.

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