Method and device for analyzing coal quality of fire coal of coal-fired boiler

Abstract

The invention discloses a method and device for analyzing the coal quality of fire coal of a coal-fired boiler. The method comprises the steps that a current operation monitoring parameter set corresponding to to-be-analyzed in-furnace fire coal is acquired, and the current operation monitoring parameter set comprises multiple operation monitoring parameters; whether the multiple operation monitoring parameters meet a preset condition or not is judged, and the preset condition is that abnormal data does not exist in the multiple operation monitoring parameters; if yes, determining a coal quality analysis result corresponding to the to-be-analyzed fired coal based on a plurality of preset formulas and a plurality of operation monitoring parameters; if not, determining a coal quality analysis result corresponding to the to-be-analyzed fired coal based on a coal quality analysis model and a plurality of operation monitoring parameters; and generating a target refined combustion operation adjustment scheme corresponding to the to-be-analyzed fired coal according to the coal quality analysis result.

Description

Technical Field

[0001] This application relates to the field of coal quality analysis technology, and in particular to a method and apparatus for analyzing the quality of coal in a coal-fired boiler. Background Technology

[0002] During the operation of coal-fired boilers in coal-fired power plants, accurate and real-time monitoring of the coal quality entering the boiler provides valuable data support for refined combustion adjustments, thereby effectively improving the power generation efficiency of coal-fired boilers, reducing pollutant emissions, and ensuring equipment safety.

[0003] Currently, the coal quality analysis of coal fed into coal-fired boilers typically employs manual sampling and offline testing. This involves workers periodically collecting coal samples manually from the conveyor belt or pulverized coal pipelines, then sending these samples to a laboratory for industrial and elemental analysis to obtain the coal quality analysis results. However, this manual sampling and offline testing method suffers from significant time lag, failing to reflect the actual coal quality being burned in the boiler and leading to severe delays in boiler operation adjustments. Furthermore, the lack of representativeness and variations in operational procedures inherent in manual sampling can easily introduce errors, making it difficult for the coal quality analysis results to accurately represent the overall condition of the coal fed into the boiler. Summary of the Invention

[0004] This application provides a method and apparatus for analyzing the quality of coal in a coal-fired boiler. The main purpose is to improve the efficiency and accuracy of analyzing the quality of coal entering the boiler during operation, thereby improving the efficiency of adjusting the operation of the coal-fired boiler.

[0005] To address the aforementioned technical problems, this application provides the following technical solutions: In a first aspect, this application provides a method for analyzing the quality of coal in a coal-fired boiler, the method comprising: Obtain the current operating monitoring parameter set corresponding to the coal to be analyzed and fed into the furnace, wherein the current operating monitoring parameter set includes multiple operating monitoring parameters; Determine whether multiple operation monitoring parameters meet preset conditions, wherein the preset conditions are that there is no abnormal data among the multiple operation monitoring parameters; If the conditions are met, the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace is determined based on multiple preset formulas and multiple operation monitoring parameters; if the conditions are not met, the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace is determined based on the coal quality analysis model and multiple operation monitoring parameters. Based on the coal quality analysis results, a target refined combustion operation adjustment plan is generated for the coal to be analyzed and fed into the furnace.

[0006] Secondly, this application also provides an apparatus for analyzing the quality of coal in a coal-fired boiler, the apparatus comprising: The acquisition unit is used to acquire the current operating monitoring parameter set corresponding to the coal to be analyzed and fed into the furnace, wherein the current operating monitoring parameter set includes multiple operating monitoring parameters; The judgment unit is used to judge whether the plurality of operation monitoring parameters meet the preset conditions, wherein the preset conditions are that there is no abnormal data among the plurality of operation monitoring parameters; The first determining unit is used to determine the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace based on multiple preset formulas and multiple operation monitoring parameters when it is determined that multiple operation monitoring parameters meet the preset conditions. The second determining unit is used to determine the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace based on the coal quality analysis model and the multiple operational monitoring parameters when it is determined that multiple operational monitoring parameters do not meet the preset conditions. The generation unit is used to generate a target refined combustion operation adjustment plan for the coal to be analyzed and fed into the furnace based on the coal quality analysis results.

[0007] Thirdly, embodiments of this application provide a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the first aspect.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0009] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in the first aspect.

[0010] By employing the above-described technical solution, the technical solution provided in this application has at least the following advantages: This application provides a method and apparatus for analyzing the quality of coal in a coal-fired boiler. After obtaining the current set of operating monitoring parameters corresponding to the coal to be analyzed from a coal quality analysis application, the application determines whether multiple operating monitoring parameters in the current set meet preset conditions. When multiple operating monitoring parameters meet the preset conditions, the application determines the coal quality analysis result corresponding to the coal to be analyzed based on multiple preset formulas and multiple operating monitoring parameters. When multiple operating monitoring parameters do not meet the preset conditions, the application determines the coal quality analysis result corresponding to the coal to be analyzed based on a coal quality analysis model and normal data from multiple operating monitoring parameters. After determining the coal quality analysis result, the application generates a target refined combustion operation adjustment plan for the coal to be analyzed and outputs and displays the target refined combustion operation adjustment plan, so that staff can adjust the operation of the target coal-fired boiler according to the target refined combustion operation adjustment plan. In this application, the coal quality analysis application determines the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace based on the real-time collected operation monitoring parameters. Therefore, it can determine the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace in a timely manner without any lag. This allows for the timely generation of a target refined combustion operation adjustment plan corresponding to the coal to be analyzed and fed into the furnace, thereby improving the efficiency of adjusting the operation of the target coal-fired boiler.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0012] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of this application are illustrated by way of example and not limitation, with the same or corresponding reference numerals denoteing the same or corresponding parts, wherein: Figure 1 A flowchart illustrating a method for analyzing the quality of coal in a coal-fired boiler, provided in an embodiment of this application, is shown. Figure 2 This application provides a flowchart of another method for analyzing the quality of coal in a coal-fired boiler, according to an embodiment of the present application. Figure 3 This paper shows a block diagram of an apparatus for analyzing the quality of coal in a coal-fired boiler, according to an embodiment of this application. Figure 4This paper presents a block diagram of another apparatus for analyzing the quality of coal in a coal-fired boiler, as provided in an embodiment of this application. Detailed Implementation

[0013] Exemplary embodiments of this application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0014] Furthermore, the terms “first,” “second,” and similar terms used in this application do not indicate any order, quantity, or importance, but are merely used to distinguish different parts.

[0015] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains.

[0016] Currently, the coal quality analysis of coal fed into coal-fired boilers typically employs manual sampling and offline testing. This involves workers periodically collecting coal samples manually from the conveyor belt or pulverized coal pipelines, then sending these samples to a laboratory for industrial and elemental analysis to obtain the coal quality analysis results. However, this manual sampling and offline testing method suffers from significant time lag, failing to reflect the actual coal quality being burned in the boiler and leading to severe delays in boiler operation adjustments. Furthermore, the lack of representativeness and variations in operational procedures inherent in manual sampling can easily introduce errors, making it difficult for the coal quality analysis results to accurately represent the overall condition of the coal fed into the boiler.

[0017] To improve the efficiency and accuracy of analyzing the quality of coal fed into a coal-fired boiler during operation, thereby increasing the efficiency of adjusting the boiler's operation, this application provides a method for analyzing the quality of coal fed into a coal-fired boiler. Figure 1 As shown.

[0018] 101. Obtain the set of current operating monitoring parameters corresponding to the coal to be analyzed and fed into the furnace.

[0019] The set of current operating monitoring parameters corresponding to the coal to be analyzed includes multiple operating monitoring parameters, which may include, but are not limited to: the standard flue gas volume, coal quantity, volumetric moisture content of flue gas at boiler outlet, excess air coefficient, SO2 concentration, CO concentration, NO concentration, oxygen content, and particulate matter concentration in flue gas at boiler outlet.

[0020] In the embodiments of this application, the execution entity in each step is a coal quality analysis application running on the target terminal device, wherein the target terminal device may be, but is not limited to, a computer, tablet computer, laptop computer, etc.

[0021] Staff at the target coal-fired power plant need to pre-install multiple sensors at the target coal-fired boiler. During the operation of the target coal-fired boiler, these sensors can collect various operational monitoring parameters corresponding to the coal fed into the boiler in real time, such as standard flue gas volume, coal consumption, and boiler outlet flue gas volumetric moisture content, etc. The collected operational monitoring parameters are then sent to the target terminal device, which stores the multiple operational monitoring parameters as a set in its local storage space. When staff need to perform coal quality analysis on the coal fed into the boiler, they can input the corresponding command into the coal quality analysis application. After receiving the command, the coal quality analysis application can retrieve the current set of operational monitoring parameters corresponding to the coal to be analyzed (i.e., the set of operational monitoring parameters whose storage time is closest to the current time) from the local storage space of the target terminal device.

[0022] 102. Determine whether multiple operation monitoring parameters meet the preset conditions.

[0023] The preset condition is that there is no abnormal data among multiple operation monitoring parameters.

[0024] Staff will pre-set the reasonable value range for each type of operation monitoring parameter based on the actual working conditions. After obtaining the current set of operation monitoring parameters corresponding to the coal to be analyzed, the coal quality analysis application needs to determine whether there is any abnormal data among the multiple operation monitoring parameters included in the current set of operation monitoring parameters. That is, it needs to determine whether each operation monitoring parameter is within its corresponding reasonable value range. When an operation monitoring parameter is within its corresponding reasonable value range, it can be determined that the operation monitoring parameter is normal data. When an operation monitoring parameter is not within its corresponding reasonable value range, it can be determined that the operation monitoring parameter is abnormal data.

[0025] 103a. When multiple operating monitoring parameters are determined to meet the preset conditions, the coal quality analysis results corresponding to the coal to be analyzed and fed into the furnace are determined based on multiple preset formulas and multiple operating monitoring parameters.

[0026] Among them, many preset formulas are set according to the law of conservation of elements.

[0027] When multiple operational monitoring parameters meet preset conditions, meaning there is no abnormal data among the multiple operational monitoring parameters, the coal quality analysis application can determine the coal quality analysis results corresponding to the coal to be analyzed and fed into the furnace based on multiple preset formulas and multiple operational monitoring parameters.

[0028] 103b. When it is determined that multiple operating monitoring parameters do not meet the preset conditions, the coal quality analysis results corresponding to the coal to be analyzed and fed into the furnace are determined based on the coal quality analysis model and multiple operating monitoring parameters.

[0029] Among them, the coal quality analysis model is used to determine the coal quality analysis results corresponding to the coal to be analyzed and fed into the furnace based on normal data from multiple operational monitoring parameters.

[0030] When multiple operational monitoring parameters are determined to be non-compliant with preset conditions, i.e., when there is abnormal data among the multiple operational monitoring parameters, the coal quality analysis application can determine the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace based on the coal quality analysis model and the normal data among the multiple operational monitoring parameters.

[0031] 104. Generate a refined combustion operation adjustment plan for the coal to be analyzed and fed into the furnace based on the coal quality analysis results.

[0032] After determining the coal quality analysis results for the coal to be analyzed and fed into the furnace, the coal quality analysis application can generate a target refined combustion operation adjustment plan for the coal to be analyzed and display it, so that the staff can adjust the operation of the target coal-fired boiler according to the target refined combustion operation adjustment plan for the coal to be analyzed and fed into the furnace.

[0033] This application provides a method for analyzing the quality of coal in a coal-fired boiler. After obtaining the current set of operating monitoring parameters corresponding to the coal to be analyzed from a coal quality analysis application, the application determines whether the multiple operating monitoring parameters in the current set meet preset conditions. When multiple operating monitoring parameters meet the preset conditions, the application determines the coal quality analysis result corresponding to the coal to be analyzed based on multiple preset formulas and multiple operating monitoring parameters. When multiple operating monitoring parameters do not meet the preset conditions, the application determines the coal quality analysis result corresponding to the coal to be analyzed based on a coal quality analysis model and normal data from the multiple operating monitoring parameters. After determining the coal quality analysis result, the application generates a target refined combustion operation adjustment plan for the coal to be analyzed and outputs and displays the target refined combustion operation adjustment plan, so that staff can adjust the operation of the target coal-fired boiler according to the target refined combustion operation adjustment plan. In this embodiment, the coal quality analysis application determines the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace based on the real-time collected operation monitoring parameters. Therefore, it can determine the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace in a timely manner without any lag. This allows for the timely generation of a target refined combustion operation adjustment plan corresponding to the coal to be analyzed and fed into the furnace, thereby improving the efficiency of adjusting the operation of the target coal-fired boiler.

[0034] To provide a more detailed explanation, this application provides another method for analyzing the quality of coal in a coal-fired boiler, as detailed below. Figure 2 As shown.

[0035] 201. Training coal quality analysis model and scheme generation model.

[0036] To ensure that the coal quality analysis application, after obtaining the current set of operational monitoring parameters corresponding to the coal to be analyzed, can accurately determine the coal quality analysis results based on the multiple operational monitoring parameters contained in the current set of operational monitoring parameters, and quickly generate a target refined combustion operation adjustment plan for the coal to be analyzed based on the coal quality analysis results, it is necessary to pre-train the coal quality analysis model and the plan generation model. The following will provide a detailed explanation of how to train and obtain the coal quality analysis model and the plan generation model.

[0037] S1: Training the coal quality analysis model.

[0038] (1) Obtain the first training sample set, wherein the first training sample set contains multiple first training samples. For any one first training sample, the first training sample contains a set of sample operation monitoring parameters corresponding to a sample coal, the sample-based sulfur content, the sample-based ash content, the sample-based carbon content, the sample-based hydrogen content, the sample-based oxygen content, the sample-based nitrogen content, the sample-based moisture content, and the sample coal quality analysis results; wherein the set of sample operation monitoring parameters may include standard flue gas volume, coal consumption, boiler outlet flue gas volumetric moisture content, and excess air. The parameters include all operational monitoring parameters such as the gas coefficient, SO2 concentration, CO concentration, NO concentration, oxygen content, and particulate matter concentration in the boiler outlet flue gas. They may also include some operational monitoring parameters from the standard flue gas volume, coal consumption, volumetric moisture content of the boiler outlet flue gas, excess air coefficient, SO2 concentration, CO concentration, NO concentration, oxygen content, and particulate matter concentration in the boiler outlet flue gas. This application does not specifically limit these parameters in its embodiments.

[0039] (2) Train the first deep learning model based on the first training sample set until the loss function corresponding to the first deep learning model converges to obtain the coal quality analysis model.

[0040] The first deep learning model can be a model built based on any existing deep learning algorithm, and this application does not specifically limit it.

[0041] The first deep learning model is iteratively trained based on a first training sample set containing multiple first training samples. In each round of training, it is determined whether the loss function of the first deep learning model has converged. If the loss function converges, the first deep learning model obtained after this round of training is determined as the coal quality analysis model. If the loss function has not converged, the model parameters of the first deep learning model are optimized and adjusted according to the loss function, and the next round of training is carried out based on the optimized and adjusted first deep learning model.

[0042] That is, after multiple rounds of iterative training of the first deep learning model based on the first training sample set, when the loss function of the first deep learning model converges, the first deep learning model at this time is determined to be the coal quality analysis model.

[0043] Because, under certain specific circumstances, even with a large number of iterative training sessions, the loss function of the first deep learning model may not converge, in order to avoid the iterative training of the first deep learning model continuing indefinitely, when it is determined that the loss function of the first deep learning model obtained after this round of training has not converged, the following two methods can be used, but are not limited to: 1. If the loss function of the first deep learning model does not converge, determine whether the current cumulative iteration training time of the first deep learning model based on the first training sample set has reached the preset time threshold.

[0044] If the current cumulative iterative training time reaches the preset time threshold, it means that the iterative training time has reached the requirement. At this time, iterative training can be stopped, and the first deep learning model obtained after this round of training can be determined as the coal quality analysis model.

[0045] If the current cumulative training duration has not reached the preset duration threshold, then the process can proceed to the next round of training, where the model parameters of the first deep learning model are optimized and adjusted according to the loss function, and the optimized and adjusted first deep learning model is used as the basis for the next round of training.

[0046] 2. If the loss function of the first deep learning model does not converge, then determine whether the current cumulative number of iterations of training the first deep learning model based on the first training sample set has reached the preset number threshold.

[0047] If the current cumulative number of training iterations reaches the preset threshold, it means that the required number of training iterations has been reached. At this point, the training iterations can be stopped, and the first deep learning model obtained after this round of training can be determined as the coal quality analysis model.

[0048] If the current cumulative number of training iterations has not reached the preset threshold, then the process can proceed to the next round of training, where the model parameters of the first deep learning model are optimized and adjusted according to the loss function, and the optimized and adjusted first deep learning model is used as the basis for the next round of training.

[0049] S2: Training scheme generates model.

[0050] (1) Obtain a second training sample set, wherein the second training sample set contains multiple second training samples. For any one second training sample, the second training sample contains the sample coal quality analysis results and the sample refined combustion operation adjustment scheme corresponding to a sample coal.

[0051] (2) Train the second deep learning model based on the second training sample set until the loss function corresponding to the second deep learning model converges to obtain the scheme generation model.

[0052] The second deep learning model can be a model built based on any existing deep learning algorithm, and this application embodiment does not specifically limit it.

[0053] The second deep learning model is iteratively trained based on a second training sample set containing multiple second training samples. In each round of training, it is determined whether the loss function of the second deep learning model has converged. If the loss function converges, the second deep learning model obtained after this round of training is determined as the scheme generation model. If the loss function has not converged, the model parameters of the second deep learning model are optimized and adjusted according to the loss function, and the next round of training is carried out based on the optimized and adjusted second deep learning model.

[0054] That is, after multiple rounds of iterative training of the second deep learning model based on the second training sample set, when the loss function of the second deep learning model converges, the second deep learning model at this time is determined to be the scheme generation model.

[0055] Because, under certain specific circumstances, even with a large number of iterative training sessions, the loss function of the second deep learning model may not converge, in order to avoid the iterative training of the second deep learning model continuing indefinitely, when it is determined that the loss function of the second deep learning model obtained after this round of training has not converged, the following two methods can be used, but are not limited to: 1. If the loss function of the second deep learning model does not converge, determine whether the current cumulative iteration training time of the second deep learning model based on the second training sample set has reached the preset time threshold.

[0056] If the current cumulative iterative training time reaches the preset time threshold, it means that the iterative training time has reached the requirement. At this time, iterative training can be stopped, and the second deep learning model obtained after this round of training can be determined as the scheme generation model.

[0057] If the current cumulative training duration has not reached the preset duration threshold, then the process can proceed to the next round of training, where the model parameters of the second deep learning model are optimized and adjusted according to the loss function, and the optimized and adjusted second deep learning model is used as the basis for the next round of training.

[0058] 2. If the loss function of the second deep learning model does not converge, determine whether the current cumulative number of iterations of training the second deep learning model based on the second training sample set has reached the preset threshold.

[0059] If the current cumulative number of training iterations reaches the preset threshold, it means that the required number of training iterations has been reached. At this point, the training iterations can be stopped, and the second deep learning model obtained after this round of training can be determined as the solution generation model.

[0060] If the current cumulative number of training iterations has not reached the preset threshold, then the process can proceed to the next round of training, where the model parameters of the second deep learning model are optimized and adjusted according to the loss function, and the optimized and adjusted second deep learning model is used as the basis for the next round of training.

[0061] 202. Obtain the set of current operating monitoring parameters corresponding to the coal to be analyzed and fed into the furnace.

[0062] Regarding step 202, obtaining the set of current operating monitoring parameters corresponding to the coal to be analyzed and fed into the furnace, please refer to the relevant description of step 101 above. This embodiment of the application will not repeat it here.

[0063] 203. Determine whether multiple operation monitoring parameters meet the preset conditions.

[0064] Regarding step 203, determining whether multiple operation monitoring parameters meet preset conditions, please refer to the relevant description of step 102 above. This application embodiment will not repeat it here.

[0065] 204a. When multiple operating monitoring parameters are determined to meet the preset conditions, the coal quality analysis results corresponding to the coal to be analyzed and fed into the furnace are determined based on multiple preset formulas and multiple operating monitoring parameters.

[0066] Among them, several operational monitoring parameters include: the standard flue gas volume corresponding to the coal to be analyzed, the coal quantity, the volumetric moisture content of the flue gas at the boiler outlet, the excess air coefficient, the SO2 concentration in the flue gas at the boiler outlet, the CO concentration in the flue gas at the boiler outlet, the NO concentration in the flue gas at the boiler outlet, the oxygen content in the flue gas at the boiler outlet, and the particulate matter concentration in the flue gas at the boiler outlet.

[0067] When multiple operational monitoring parameters meet preset conditions, meaning there is no abnormal data among the multiple operational monitoring parameters, the coal quality analysis application can determine the coal quality analysis results corresponding to the coal to be analyzed and fed into the furnace based on multiple preset formulas and multiple operational monitoring parameters.

[0068] Specifically, in this step, the coal quality analysis application determines the coal quality analysis results corresponding to the coal to be analyzed and fed into the furnace based on multiple preset formulas and multiple operational monitoring parameters as follows: (1) First, obtain the set of constant terms corresponding to the coal to be analyzed and fed into the furnace. The set of constant terms includes the coefficient of sulfur in coal to SO2 and the ratio of fly ash to total ash and slag.

[0069] (2) Next, the coefficient of sulfur in coal to SO2, the standard flue gas volume corresponding to the target coal to be fed into the furnace, the coal volume and the SO2 concentration in the flue gas at the boiler outlet are substituted into the first preset formula to calculate the received basic sulfur content corresponding to the target coal to be fed into the furnace.

[0070] (3) Next, the proportion of fly ash to total ash and slag, the standard flue gas volume corresponding to the coal to be analyzed, the coal volume, and the particulate matter concentration in the flue gas at the boiler outlet are substituted into the second preset formula to calculate the received ash content corresponding to the coal to be analyzed.

[0071] (4) Then, the standard flue gas volume, coal volume, boiler outlet flue gas volumetric moisture content, excess air coefficient, SO2 concentration, CO concentration, NO concentration, oxygen content, as-received sulfur content, and as-received ash content corresponding to the target coal are substituted into the preset positive definite equation set to calculate the as-received carbon content, as-received hydrogen content, as-received oxygen content, as-received nitrogen content, and as-received moisture content corresponding to the target coal.

[0072] (5) Finally, the as-received sulfur content, as-received ash content, as-received carbon content, as-received hydrogen content, as-received oxygen content, as-received nitrogen content and as-received moisture content of the coal to be analyzed are determined as the coal quality analysis results of the coal to be analyzed.

[0073] The first preset formula is as follows: 0.007S ar =(Q* C SO2 )*(22.4 / 64)*10 -6 / (k SO2 *B*1000) Among them, S ar Let Q be the received basis sulfur content of the target coal fed into the furnace, and C be the standard flue gas volume corresponding to the target coal fed into the furnace. SO2 k represents the SO2 concentration in the boiler outlet flue gas corresponding to the target coal input. SO2 denoted as the coefficient for the conversion of sulfur in coal to SO2, and B is the amount of coal required to fuel the target furnace.

[0074] The second preset formula is as follows: A ar =(Q* C dust *10 -7 ) / (B*r) Among them, A ar Let Q be the received ash content of the coal to be analyzed, and let C be the standard flue gas volume corresponding to the coal to be analyzed. dust Let B be the particulate matter concentration in the boiler outlet flue gas corresponding to the coal to be analyzed, B be the amount of coal to be analyzed, and r be the ratio of fly ash to total ash and slag.

[0075] The pre-set positive definite equation system is as follows: C ar +H ar +O ar +N ar +S ar +A ar +M ar =100 0.11524Har +0.0124M ar +0.001424(C ar +0.375S ar -0.0005328 O ar =(Q*X sw ) / (B*1000) (0.037334-0.0374λ) C ar -0.014λS ar -0.014(1-λ) O ar -0.0124M ar -0.1113λH ar =X X=(Q*C) CO )*(22.4 / 28)*10 -6 / (B*1000)-( Q*X sw ) / (B*1000)-(2* Q*O2) / (B*1000)- (2* Q* C SO2 )*(22.4 / 64)*10 -6 / (B*1000)-(Q*C NO )*(22.4 / 34)*10 -6 / (B*1000) O ar *Y=202.276592-3.81184*C ar *Y+0.01777704*(C ar *Y)^2 H ar *Y=-52.867025+1.49843546* C ar *Y-0.00961601* (C ar *Y)^2 Y=(100-A ar -M ar ) Among them, C ar To analyze the received carbon content of the coal to be fed into the furnace, H ar To analyze the received basis hydrogen content of the coal to be fed into the furnace, O ar To analyze the received basis oxygen content of the coal to be fed into the furnace, N ar To analyze the received basis nitrogen content of the coal to be fed into the furnace, S ar To analyze the received basis sulfur content of the coal to be fed into the furnace, A ar To analyze the received ash content of the coal to be fed into the furnace, M arLet X be the received basis moisture content of the coal to be analyzed, Q be the standard flue gas volume corresponding to the coal to be analyzed, and X be the standard state moisture content. sw Let B be the volumetric moisture content of the boiler outlet flue gas corresponding to the coal to be analyzed, B be the coal quantity corresponding to the coal to be analyzed, λ be the excess air coefficient corresponding to the coal to be analyzed, and C be the excess air coefficient corresponding to the coal to be analyzed. SO2 To determine the SO2 concentration in the boiler outlet flue gas corresponding to the coal to be fed into the furnace, C CO To determine the CO concentration in the boiler outlet flue gas corresponding to the coal to be fed into the furnace, C NO The NO concentration in the boiler outlet flue gas corresponding to the coal to be analyzed is denoted as O2, and the O2 concentration in the boiler outlet flue gas corresponding to the coal to be analyzed is denoted as O2.

[0076] 204b. When it is determined that multiple operating monitoring parameters do not meet the preset conditions, the coal quality analysis results corresponding to the coal to be analyzed and fed into the furnace are determined based on the coal quality analysis model and the multiple operating monitoring parameters.

[0077] Multiple operational monitoring parameters include: standard flue gas volume corresponding to the coal to be analyzed, coal quantity, volumetric moisture content of flue gas at boiler outlet, excess air coefficient, SO2 concentration in flue gas at boiler outlet, CO concentration in flue gas at boiler outlet, NO concentration in flue gas at boiler outlet, oxygen content in flue gas at boiler outlet, and particulate matter concentration in flue gas at boiler outlet.

[0078] When multiple operational monitoring parameters are determined to be non-compliant with preset conditions, i.e., when there is abnormal data among the multiple operational monitoring parameters, the coal quality analysis application can determine the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace based on the coal quality analysis model and the normal data among the multiple operational monitoring parameters.

[0079] Specifically, in this step, the coal quality analysis application determines the coal quality analysis results corresponding to the coal to be analyzed and fed into the furnace based on the coal quality analysis model and multiple operational monitoring parameters as follows: (1) First, abnormal data in multiple operation monitoring parameters are removed.

[0080] (2) Next, the multiple operational monitoring parameters after rejection processing are input into the coal quality analysis model. After receiving the multiple operational monitoring parameters after rejection processing, the coal quality analysis model can determine the received sulfur content, received ash content, received carbon content, received hydrogen content, received oxygen content, received nitrogen content and received moisture content of the coal to be analyzed and fed into the furnace based on the multiple operational monitoring parameters after rejection processing. The received sulfur content, received ash content, received carbon content, received hydrogen content, received oxygen content, received nitrogen content and received moisture content of the coal to be analyzed and fed into the furnace are determined as the coal quality analysis results of the coal to be analyzed and fed into the furnace.

[0081] 205. Generate a refined combustion operation adjustment plan for the coal to be analyzed and fed into the furnace based on the coal quality analysis results.

[0082] After determining the coal quality analysis results for the coal to be analyzed and fed into the furnace, the coal quality analysis application can generate a target refined combustion operation adjustment plan for the coal to be analyzed and display it, so that the staff can adjust the operation of the target coal-fired boiler according to the target refined combustion operation adjustment plan for the coal to be analyzed and fed into the furnace.

[0083] Specifically, in this step, the coal quality analysis application can input the coal quality analysis results corresponding to the coal to be analyzed into the scheme generation model. After receiving the coal quality analysis results corresponding to the coal to be analyzed, the scheme generation model can generate a target refined combustion operation adjustment scheme for the coal to be analyzed based on the coal quality analysis results.

[0084] Furthermore, as a response to the above Figure 1 and Figure 2 In addition to the methods described above, another embodiment of this application provides an apparatus for analyzing the quality of coal in a coal-fired boiler. This apparatus embodiment corresponds to the aforementioned method embodiment. For ease of reading, this apparatus embodiment will not repeat the details of the aforementioned method embodiment, but it should be understood that the apparatus in this embodiment can implement all the contents of the aforementioned method embodiment. This apparatus is used to improve the efficiency and accuracy of analyzing the quality of coal entering the boiler during the operation of a coal-fired boiler, thereby improving the efficiency of adjusting the operation of the coal-fired boiler. Specifically, as shown... Figure 3 As shown, the device includes: The acquisition unit 31 is used to acquire the current operating monitoring parameter set corresponding to the coal to be analyzed and fed into the furnace, wherein the current operating monitoring parameter set includes multiple operating monitoring parameters; The judgment unit 32 is used to judge whether the plurality of operation monitoring parameters meet the preset conditions, wherein the preset conditions are that there is no abnormal data among the plurality of operation monitoring parameters; The first determining unit 33 is used to determine the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace based on multiple preset formulas and multiple operating monitoring parameters when it is determined that multiple operating monitoring parameters meet the preset conditions. The second determining unit 34 is used to determine the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace based on the coal quality analysis model and the multiple operating monitoring parameters when it is determined that multiple operating monitoring parameters do not meet the preset conditions. The generation unit 35 is used to generate a target refined combustion operation adjustment scheme for the coal to be analyzed and fed into the furnace based on the coal quality analysis results.

[0085] Furthermore, such as Figure 4 As shown, the multiple operational monitoring parameters include: the standard flue gas volume corresponding to the coal to be analyzed, the coal quantity, the volumetric moisture content of the flue gas at the boiler outlet, the excess air coefficient, the SO2 concentration in the flue gas at the boiler outlet, the CO concentration in the flue gas at the boiler outlet, the NO concentration in the flue gas at the boiler outlet, the oxygen content in the flue gas at the boiler outlet, and the particulate matter concentration in the flue gas at the boiler outlet; the first determining unit 33 is specifically used for: Obtain the set of constant terms corresponding to the coal to be analyzed and fed into the furnace, wherein the set of constant terms includes the coefficient of sulfur conversion to SO2 in the coal and the ratio of fly ash to total ash and slag; The standard flue gas volume, the coal consumption, the coefficient of sulfur conversion to SO2 in the coal, and the SO2 concentration in the boiler outlet flue gas are substituted into the first preset formula to calculate the received basis sulfur content corresponding to the target coal fed into the furnace. The fly ash ratio, the standard flue gas volume, the coal consumption, and the particulate matter concentration in the boiler outlet flue gas are substituted into the second preset formula to calculate the received ash content of the coal to be analyzed. The standard flue gas volume, the coal consumption, the volumetric moisture content of the boiler outlet flue gas, the excess air coefficient, the SO2 concentration, CO concentration, NO concentration, oxygen content, sulfur content, and ash content of the boiler outlet flue gas are substituted into a preset positive definite equation set to calculate the carbon content, hydrogen content, oxygen content, nitrogen content, and moisture content of the target coal fed into the furnace. The as-received sulfur content, as-received ash content, as-received carbon content, as-received hydrogen content, as-received oxygen content, as-received nitrogen content, and as-received moisture content of the coal to be analyzed are determined as the coal quality analysis results.

[0086] Furthermore, such as Figure 4 As shown, the multiple operational monitoring parameters include: the standard flue gas volume corresponding to the coal to be analyzed, the coal quantity, the volumetric moisture content of the flue gas at the boiler outlet, the excess air coefficient, the SO2 concentration in the flue gas at the boiler outlet, the CO concentration in the flue gas at the boiler outlet, the NO concentration in the flue gas at the boiler outlet, the oxygen content in the flue gas at the boiler outlet, and the particulate matter concentration in the flue gas at the boiler outlet; the second determining unit 34 is specifically used for: Abnormal data in multiple of the aforementioned operational monitoring parameters are removed. The multiple operational monitoring parameters, after being filtered out, are input into the coal quality analysis model to determine the coal quality analysis results; The coal quality analysis model is used to determine the as-received sulfur content, as-received ash content, as-received carbon content, as-received hydrogen content, as-received oxygen content, as-received nitrogen content, and as-received moisture content of the coal to be analyzed, based on multiple operational monitoring parameters after rejection processing. The as-received sulfur content, as-received ash content, as-received carbon content, as-received hydrogen content, as-received oxygen content, as-received nitrogen content, and as-received moisture content are then determined as the coal quality analysis results.

[0087] Furthermore, such as Figure 4 As shown, the device also includes: The first training unit 36 ​​is used to acquire a first training sample set, wherein the first training sample set contains multiple first training samples, and the first training samples contain a set of sample operation monitoring parameters corresponding to the sample coal, the sample received sulfur content, the sample received ash content, the sample received carbon content, the sample received hydrogen content, the sample received oxygen content, the sample received nitrogen content, the sample received moisture content, and the sample coal quality analysis results. The first deep learning model is iteratively trained based on the first training sample set; wherein... In each round of training, determine whether the loss function of the first deep learning model has converged; If the loss function converges, the first deep learning model obtained after this round of training is determined as the coal quality analysis model; If the loss function does not converge, the model parameters of the first deep learning model are optimized and adjusted according to the loss function, and the first deep learning model enters the next round of training based on the optimized and adjusted model.

[0088] Furthermore, such as Figure 4 As shown, generation unit 35 is specifically used for: The coal quality analysis results are input into the scheme generation model to generate the target refined combustion operation adjustment scheme; The scheme generation model is used to generate the target refined combustion operation adjustment scheme based on the coal quality analysis results.

[0089] Furthermore, such as Figure 4 As shown, the device also includes: The second training unit 37 is used to acquire a second training sample set, wherein the second training sample set contains multiple second training samples, and the second training samples contain sample coal quality analysis results and sample refined combustion operation adjustment schemes corresponding to sample coal. The second deep learning model is iteratively trained based on the second training sample set; wherein... In each round of training, it is determined whether the loss function of the second deep learning model has converged; If the loss function converges, the second deep learning model obtained after this round of training is determined as the scheme generation model; If the loss function does not converge, the model parameters of the second deep learning model are optimized and adjusted according to the loss function, and the second deep learning model enters the next round of training based on the optimized and adjusted model.

[0090] This application provides a method and apparatus for analyzing the quality of coal in a coal-fired boiler. After obtaining the current set of operating monitoring parameters corresponding to the coal to be analyzed from a coal quality analysis application, the application determines whether the multiple operating monitoring parameters in the current set meet preset conditions. When multiple operating monitoring parameters meet the preset conditions, the application determines the coal quality analysis result corresponding to the coal to be analyzed based on multiple preset formulas and multiple operating monitoring parameters. When multiple operating monitoring parameters do not meet the preset conditions, the application determines the coal quality analysis result corresponding to the coal to be analyzed based on a coal quality analysis model and normal data from the multiple operating monitoring parameters. After determining the coal quality analysis result, the application generates a target refined combustion operation adjustment plan for the coal to be analyzed based on the coal quality analysis result, and outputs and displays the target refined combustion operation adjustment plan so that staff can adjust the operation of the target coal-fired boiler according to the target refined combustion operation adjustment plan. In this embodiment, the coal quality analysis application determines the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace based on the real-time collected operation monitoring parameters. Therefore, it can determine the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace in a timely manner without any lag. This allows for the timely generation of a target refined combustion operation adjustment plan corresponding to the coal to be analyzed and fed into the furnace, thereby improving the efficiency of adjusting the operation of the target coal-fired boiler.

[0091] This application provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the method for analyzing the coal quality of a coal-fired boiler as described above.

[0092] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method for analyzing the coal quality of coal-fired boilers as described above.

[0093] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method for analyzing the quality of coal in a coal-fired boiler as described above.

[0094] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0098] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0099] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0100] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0101] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0102] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for analyzing the quality of coal in a coal-fired boiler, characterized in that, The method includes: Obtain the current operating monitoring parameter set corresponding to the coal to be analyzed and fed into the furnace, wherein the current operating monitoring parameter set includes multiple operating monitoring parameters; Determine whether multiple operation monitoring parameters meet preset conditions, wherein the preset conditions are that there is no abnormal data among the multiple operation monitoring parameters; If the conditions are met, the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace is determined based on multiple preset formulas and multiple operation monitoring parameters; if the conditions are not met, the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace is determined based on the coal quality analysis model and multiple operation monitoring parameters. Based on the coal quality analysis results, a target refined combustion operation adjustment plan is generated for the coal to be analyzed and fed into the furnace.

2. The method according to claim 1, characterized in that, The multiple operational monitoring parameters include: the standard flue gas volume, coal consumption, boiler outlet flue gas volumetric moisture content, excess air coefficient, SO2 concentration, CO concentration, NO concentration, oxygen content, and particulate matter concentration in the boiler outlet flue gas corresponding to the coal to be analyzed; the determination of the coal quality analysis results corresponding to the coal to be analyzed based on multiple preset formulas and multiple operational monitoring parameters includes: Obtain the set of constant terms corresponding to the coal to be analyzed and fed into the furnace, wherein the set of constant terms includes the coefficient of sulfur conversion to SO2 in the coal and the ratio of fly ash to total ash and slag; The standard flue gas volume, the coal consumption, the coefficient of sulfur conversion to SO2 in the coal, and the SO2 concentration in the boiler outlet flue gas are substituted into the first preset formula to calculate the received basis sulfur content corresponding to the target coal fed into the furnace. The fly ash ratio, the standard flue gas volume, the coal consumption, and the particulate matter concentration in the boiler outlet flue gas are substituted into the second preset formula to calculate the received ash content of the coal to be analyzed. The standard flue gas volume, the coal consumption, the volumetric moisture content of the boiler outlet flue gas, the excess air coefficient, the SO2 concentration, CO concentration, NO concentration, oxygen content, sulfur content, and ash content of the boiler outlet flue gas are substituted into a preset positive definite equation set to calculate the carbon content, hydrogen content, oxygen content, nitrogen content, and moisture content of the target coal fed into the furnace. The as-received sulfur content, as-received ash content, as-received carbon content, as-received hydrogen content, as-received oxygen content, as-received nitrogen content, and as-received moisture content of the coal to be analyzed are determined as the coal quality analysis results.

3. The method according to claim 1, characterized in that, The multiple operational monitoring parameters include: the standard flue gas volume, coal consumption, boiler outlet flue gas volumetric moisture content, excess air coefficient, SO2 concentration, CO concentration, NO concentration, oxygen content, and particulate matter concentration in the boiler outlet flue gas corresponding to the coal to be analyzed; the determination of the coal quality analysis results corresponding to the coal to be analyzed based on the coal quality analysis model and the multiple operational monitoring parameters includes: Abnormal data in multiple of the aforementioned operational monitoring parameters are removed. The multiple operational monitoring parameters, after being filtered out, are input into the coal quality analysis model to determine the coal quality analysis results; The coal quality analysis model is used to determine the as-received sulfur content, as-received ash content, as-received carbon content, as-received hydrogen content, as-received oxygen content, as-received nitrogen content, and as-received moisture content of the coal to be analyzed, based on multiple operational monitoring parameters after rejection processing. The as-received sulfur content, as-received ash content, as-received carbon content, as-received hydrogen content, as-received oxygen content, as-received nitrogen content, and as-received moisture content are then determined as the coal quality analysis results.

4. The method according to claim 3, characterized in that, The method further includes: Obtain a first training sample set, wherein the first training sample set contains multiple first training samples, and the first training samples contain a set of sample operation monitoring parameters corresponding to the sample coal, the sample received sulfur content, the sample received ash content, the sample received carbon content, the sample received hydrogen content, the sample received oxygen content, the sample received nitrogen content, the sample received moisture content, and the sample coal quality analysis results. The first deep learning model is iteratively trained based on the first training sample set; wherein... In each round of training, determine whether the loss function of the first deep learning model has converged; If the loss function converges, the first deep learning model obtained after this round of training is determined as the coal quality analysis model; If the loss function does not converge, the model parameters of the first deep learning model are optimized and adjusted according to the loss function, and the first deep learning model enters the next round of training based on the optimized and adjusted model.

5. The method according to any one of claims 1-4, characterized in that, The step of generating a target refined combustion operation adjustment plan for the coal to be analyzed and fed into the furnace based on the coal quality analysis results includes: The coal quality analysis results are input into the scheme generation model to generate the target refined combustion operation adjustment scheme; The scheme generation model is used to generate the target refined combustion operation adjustment scheme based on the coal quality analysis results.

6. The method according to claim 5, characterized in that, The method further includes: Obtain a second training sample set, wherein the second training sample set contains multiple second training samples, and the second training samples contain sample coal quality analysis results and sample refined combustion operation adjustment schemes corresponding to sample coal; The second deep learning model is iteratively trained based on the second training sample set; wherein... In each round of training, it is determined whether the loss function of the second deep learning model has converged; If the loss function converges, the second deep learning model obtained after this round of training is determined as the scheme generation model; If the loss function does not converge, the model parameters of the second deep learning model are optimized and adjusted according to the loss function, and the second deep learning model enters the next round of training based on the optimized and adjusted model.

7. An apparatus for analyzing the quality of coal in a coal-fired boiler, characterized in that, The device includes: The acquisition unit is used to acquire the current operating monitoring parameter set corresponding to the coal to be analyzed and fed into the furnace, wherein the current operating monitoring parameter set includes multiple operating monitoring parameters; The judgment unit is used to judge whether the plurality of operation monitoring parameters meet the preset conditions, wherein the preset conditions are that there is no abnormal data among the plurality of operation monitoring parameters; The first determining unit is used to determine the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace based on multiple preset formulas and multiple operation monitoring parameters when it is determined that multiple operation monitoring parameters meet the preset conditions. The second determining unit is used to determine the coal quality analysis result corresponding to the coal to be analyzed and fed into the furnace based on the coal quality analysis model and the multiple operational monitoring parameters when it is determined that multiple operational monitoring parameters do not meet the preset conditions. The generation unit is used to generate a target refined combustion operation adjustment plan for the coal to be analyzed and fed into the furnace based on the coal quality analysis results.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images