Boiler combustion regulation method and system

Abstract

The present application provides a kind of boiler combustion regulation method and system, belong to boiler combustion technical field.The method includes: in each primary air pipe carries out coal powder online sampling, carries out coal quality detection to online sampling coal powder, obtains coal quality detection result;Wherein, coal quality detection result includes coal powder sulfur content data and coal powder calorific value data;Based on the sulfur content data of each primary air pipe, periodic statistics is carried out, corresponding sulfur content evaluation coefficient and corresponding sulfur content rising rate are obtained;Based on correction algorithm, the coal powder calorific value data of each primary air pipe is processed, corresponding coal supply correction coefficient is obtained;Based on the coal supply correction coefficient of each primary air pipe, the sulfur content evaluation coefficient of each primary air pipe and the sulfur content rising rate of each primary air pipe, boiler combustion control instruction is generated and executed.Implemented coal powder online sampling, online detection and online boiler combustion adjustment effect, to realize the purpose that the supply amount of fuel and the heat load demand of boiler match.

Description

Technical Field

[0001] This invention relates to the field of boiler combustion technology, specifically to a boiler combustion control method, a boiler combustion control system, a machine-readable storage medium, and an electronic device. Background Technology

[0002] In the context of carbon neutrality, deep and flexible peak shaving of thermal power is the most economical and effective means to effectively alleviate grid instability caused by new energy sources. However, under the background of deep peak shaving, coal-fired units need to solve the following problems in adapting to rapid and accurate response to AGC load commands: First, most coal-fired units currently use mixed coal combustion, resulting in unstable calorific value of the coal fed into the furnace, and low calorific value correction accuracy, which leads to large load control deviations, unstable main parameters, and regulation rates that do not meet grid requirements; second, safety risks increase, with a higher rate of boiler flameout, boiler heating surface overheating, and wet steam occurrence in the turbine; third, the flexibility of boilers decreases under low-NOx combustion.

[0003] Existing technology provides a boiler combustion control system that enables online coal quality monitoring to optimize coal feed rate adjustment, reducing the variation in coal combustion caused by unit load fluctuations and preventing boiler coking and corrosion. However, it does not solve the problem of how to utilize online coal quality monitoring data to optimize boiler combustion.

[0004] Therefore, for coal-fired power units, a pressing issue is to provide a boiler combustion adjustment and control method based on online coal quality monitoring data, improving the speed and accuracy of real-time fuel quantity correction. Improved fuel quantity correction technology can adjust and calibrate the fuel supply during boiler combustion to ensure stability and efficiency, ultimately matching the fuel supply with the boiler's heat load requirements for optimal combustion. Simultaneously, it enables the diagnosis of corrosion conditions in the boiler's water-cooled walls, facilitating better boiler maintenance. Summary of the Invention

[0005] The purpose of this invention is to provide a boiler combustion control method and system to at least solve the aforementioned problems of failing to achieve online pulverized coal sampling, online detection, and online boiler combustion adjustment.

[0006] To achieve the above objectives, a first aspect of the present invention provides a boiler combustion control method, comprising: Coal powder was sampled online in each primary air duct, and the sampled coal powder was subjected to coal quality testing to obtain coal quality test results. The coal quality test results include coal powder sulfur content data and coal powder calorific value data. Periodic statistics were performed on the sulfur content data of pulverized coal in each primary air duct to obtain the corresponding sulfur content assessment coefficient and the corresponding sulfur content increase rate. The calorific value data of pulverized coal in each primary air duct is processed based on the correction algorithm to obtain the corresponding coal feed correction coefficient. Based on the coal feed correction coefficient, sulfur content assessment coefficient, and sulfur content rise rate of each primary air duct, boiler combustion control commands are generated and executed.

[0007] Optionally, the above boiler combustion control instructions include fuel control commands; The rules for generating fuel control commands include: The coal feed correction coefficient corresponding to each primary air duct is imported into the coal feed feedback monitoring loop of the corresponding combustion layer coal feeder to perform a multiplication operation on the measured coal feed Fn to obtain the coal feed feedback value Fnr of the corresponding combustion layer coal feeder; where n = positive integer; The total boiler coal feed rate Fr is obtained by summing the coal feeder feedback values ​​of all combustion chamber coal feeders; the summation formula is as follows: ;

[0008] The total coal feed rate Fr of the boiler is used as the feedback quantity in the fuel control loop to generate fuel control commands.

[0009] Optionally, the above boiler combustion control commands include an alarm command for abnormal sulfur rise rate; The rules for generating alarm commands for abnormal sulfur content rise rate include: The sulfur rise rate corresponding to each primary air duct is compared with the preset sulfur rise standard rate. If the sulfur content rise rate of the primary air duct is greater than the preset sulfur content rise standard rate, an abnormal sulfur content rise rate alarm command will be generated for that primary air duct.

[0010] Optionally, the above boiler combustion control commands include an alarm command for abnormal sulfur content assessment coefficient; The rules for generating alarm commands for abnormal sulfur content assessment coefficients include: The sulfur content assessment coefficients for each primary air duct are compared with the preset sulfur content standard values. If the sulfur content assessment coefficient of a primary air duct is greater than the preset sulfur content standard value, an abnormal sulfur content assessment coefficient alarm command will be generated for that primary air duct.

[0011] Optionally, the above-mentioned periodic statistics based on the sulfur content data of pulverized coal in each primary air duct are used to obtain the corresponding sulfur content assessment coefficient and the corresponding sulfur content rise rate, including: Periodic statistics were performed on the sulfur content data of pulverized coal in each primary air duct to obtain the corresponding data lake; Calculate the mean and standard deviation of the data lake corresponding to each primary air duct; Based on the time series characteristics of the data lake, predict the sulfur content of pulverized coal; Based on the average value, standard deviation, and sulfur content prediction results of the data lake corresponding to each primary air duct, the sulfur content assessment coefficient and sulfur content rise rate corresponding to each primary air duct are obtained.

[0012] Optionally, the above-mentioned prediction of pulverized coal sulfur content based on the time series characteristics of the data lake includes: Based on the time series characteristics of the data lake, a prediction model for sulfur content in pulverized coal was established and trained. The sulfur content of pulverized coal is predicted using a trained pulverized coal sulfur content prediction model.

[0013] Optionally, the above correction algorithms include the mean algorithm and the standard deviation algorithm; The above-mentioned correction algorithm is used to process the pulverized coal calorific value data of each primary air duct to obtain the corresponding coal feed rate correction coefficient, including: The calorific value data of pulverized coal for each primary air duct are processed using the mean algorithm and the standard deviation algorithm to obtain the coal feed correction coefficient for each primary air duct.

[0014] A second aspect of the present invention provides a boiler combustion control system, comprising: The coal powder sampling and testing module is used to take online coal powder samples in each primary air duct, and to conduct coal quality testing on the online coal powder samples to obtain coal quality test results; among which, the coal quality test results include coal powder sulfur content data and coal powder calorific value data; The coal powder sulfur content data statistics module is used to perform periodic statistics on coal powder sulfur content data based on each primary air duct, and obtain the corresponding sulfur content evaluation coefficient and the corresponding sulfur content rise rate. The pulverized coal calorific value data processing module is used to process the pulverized coal calorific value data of each primary air duct based on the correction algorithm to obtain the corresponding coal feed correction coefficient. The boiler combustion control command generation module is used to generate and execute boiler combustion control commands based on the coal feed correction coefficient, sulfur content assessment coefficient, and sulfur content rise rate of each primary air duct.

[0015] In a third aspect, the present invention provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the boiler combustion control method described above.

[0016] In a fourth aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned boiler combustion control method.

[0017] The above technical solution provides a boiler combustion control method and system that performs online sampling of pulverized coal in each primary air duct and conducts real-time coal quality testing on the sampled pulverized coal to obtain the coal quality test results. By periodically statistically analyzing the sulfur content data of pulverized coal in the coal quality test results of each primary air duct, the corresponding sulfur content assessment coefficient and the corresponding sulfur content rise rate are obtained to guide boiler coal blending and maintenance. A correction algorithm is used to process the calorific value data of pulverized coal in the coal quality test results of each primary air duct to obtain the corresponding coal feed correction coefficient, which is used to guide boiler combustion optimization and adjustment. Based on the coal feed correction coefficient, sulfur content assessment coefficient, and sulfur content rise rate corresponding to each primary air duct, boiler combustion control commands are generated and executed to achieve the purpose of boiler coal blending and combustion adjustment. This method and system achieve online sampling, online detection, and online boiler combustion adjustment of pulverized coal, avoiding the lag caused by traditional sampling and measurement. This enables the fuel supply to match the boiler's heat load demand, significantly improving the unit's response capability to AGC load commands across the entire load range, especially under deep adjustment conditions.

[0018] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a boiler combustion control method provided in one embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the execution of another boiler combustion control method provided in one embodiment of the present invention; Figure 3 This is a block diagram of a boiler combustion control system provided in one embodiment of the present invention; Figure 4 This is a schematic diagram of an electronic device structure provided by a preferred embodiment of the present invention.

[0020] Explanation of reference numerals in the attached figures 10 - Electronic device, 100 - Processor, 101 - Memory, 102 - Computer program. Detailed Implementation

[0021] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0022] Figure 1 This is a flowchart of a boiler combustion control method according to one embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the execution of another boiler combustion control method provided in one embodiment of the present invention. Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a boiler combustion control method, comprising: S110: Online sampling of pulverized coal is carried out in each primary air duct, and the online sampled pulverized coal is subjected to coal quality testing to obtain coal quality test results; among which, the coal quality test results include pulverized coal sulfur content data and pulverized coal calorific value data; In some embodiments of this example, the specific process of online coal powder sampling in each primary air duct and coal quality testing of the online sampled coal powder is as follows: Step 1: Coal powder is sampled online using online sampling tubes installed on different primary air ducts. The online sampling tubes are numbered P1, P2...Pn. After the same sampling time interval T, the samples taken by each online sampling tube are numbered C1, C2...Cn. Once sampling is completed, it is considered that one sampling cycle has been completed. Then, sampling is repeated from the first primary air duct, thereby realizing the cyclic sampling of all primary air ducts. Step 2: The coal powder samples C1, C2...Cn obtained online are sequentially transported to the online rapid coal quality detection system for real-time coal powder sample detection. The coal powder sample detection order corresponds to the sample order obtained in Step 1, realizing real-time circulating coal powder sample detection. Step 3: Real-time detection of coal powder sulfur content and calorific value data of coal powder samples C1, C2...Cn using an online rapid coal quality detection system. The coal powder sulfur content data are recorded as S1, S2...Sn, and the coal powder calorific value data are recorded as Q1, Q2...Qn.

[0023] Specifically, by taking online samples and detecting the sulfur content of coal samples entering the furnace in real time, the system can provide online guidance for boiler coal blending and combustion, avoiding the lag caused by traditional sampling and measurement, and reducing the pressure on the safe, stable and compliant operation of subsequent environmental protection facilities.

[0024] S120: Based on the sulfur content data of pulverized coal in each primary air duct, periodic statistics are performed to obtain the corresponding sulfur content evaluation coefficient and the corresponding sulfur content rise rate. In some embodiments of this example, the above-mentioned periodic statistical analysis of the sulfur content data of pulverized coal in each primary air duct to obtain the corresponding sulfur content assessment coefficient and the corresponding sulfur content rise rate includes: performing periodic statistical analysis on the sulfur content data of pulverized coal in each primary air duct to obtain the corresponding data lake; calculating the average value and standard deviation of the data lake corresponding to each primary air duct; predicting the sulfur content data of pulverized coal based on the time series characteristics of the data lake; and obtaining the sulfur content assessment coefficient and sulfur content rise rate corresponding to each primary air duct based on the average value, standard deviation and sulfur content data prediction results of the data lake corresponding to each primary air duct.

[0025] Specifically, periodic statistics are performed on the sulfur content data of pulverized coal for each primary air duct to form a data lake for each duct. A comprehensive algorithm combining mean and standard deviation and time series analysis is used to calculate the mean and standard deviation of the data lake for each primary air duct to understand its central trend and distribution. Subsequently, based on the time series characteristics of the data lake for each primary air duct, the sulfur content data of pulverized coal for each primary air duct is predicted, forming a sulfur content evaluation coefficient S for the primary air duct. E1 S E2 ......S En and the rate of increase of sulfur content V E1 V E2 ......V En The sulfur content assessment coefficient and corresponding sulfur content rise rate for each primary air duct can be used to guide boiler coal blending and maintenance. Regular sulfur content data statistics are used for the diagnosis and assessment of boiler water-cooled wall corrosion status, facilitating targeted maintenance of water-cooled wall equipment.

[0026] In some embodiments of this example, the above-mentioned prediction of pulverized coal sulfur content based on the time series characteristics of the data lake includes: establishing and training a pulverized coal sulfur content prediction model based on the time series characteristics of the data lake; and using the trained pulverized coal sulfur content prediction model to predict pulverized coal sulfur content.

[0027] Specifically, the time series characteristics of the data lake corresponding to each primary air duct are modeled and predicted to establish and train a coal powder sulfur content prediction model for each primary air duct, thereby realizing the prediction of coal powder sulfur content data for each primary air duct through the coal powder sulfur content prediction model for each primary air duct.

[0028] S130: The calorific value data of pulverized coal in each primary air duct is processed based on the correction algorithm to obtain the corresponding coal feed correction coefficient; In some embodiments of this example, the above-mentioned correction algorithm includes a mean algorithm and a standard deviation algorithm; the above-mentioned processing of the pulverized coal calorific value data of each primary air duct based on the correction algorithm to obtain the corresponding coal feed correction coefficient includes: processing the pulverized coal calorific value data of each primary air duct through the mean algorithm and the standard deviation algorithm to obtain the corresponding coal feed correction coefficient for each primary air duct.

[0029] Specifically, after processing the pulverized coal calorific value data of each primary air duct using the mean algorithm and standard deviation algorithm, a coal feed correction coefficient corresponding to each primary air duct can be generated. The coal feed correction coefficient can be used to guide the optimization and adjustment of boiler combustion.

[0030] S140: Based on the coal feed correction coefficient, sulfur content assessment coefficient, and sulfur content rise rate of each primary air duct, generate and execute boiler combustion control commands.

[0031] In some embodiments of this example, the boiler combustion control command includes a fuel control command; the generation rule of the fuel control command includes: importing the coal feed rate correction coefficient corresponding to each primary air duct into the coal feed rate feedback monitoring loop of the corresponding combustion layer coal feeder, to perform a multiplication operation on the measured coal feed rate Fn, to obtain the corresponding combustion layer coal feeder coal feed rate feedback value Fnr; where n = positive integer; summing all combustion layer coal feeder coal feed rate feedback values ​​to obtain the total boiler coal feed rate Fr; where the summation formula is: The total coal feed rate Fr of the boiler is used as the feedback quantity in the fuel control loop to generate fuel control commands.

[0032] Specifically, the total boiler coal feed rate Fr is used as a feedback quantity in the fuel control loop to participate in fuel control. The generated fuel control commands are transmitted to the DCS combustion system for real-time control of the pulverizing system, optimizing and adjusting the coal feeder rate, thereby achieving online optimization and adjustment of boiler combustion. By providing real-time feedback and correction of changes in the calorific value of the fuel entering the furnace, the stability of the unit's control system is greatly improved.

[0033] In some embodiments of this example, the boiler combustion control command includes an abnormal sulfur rise rate alarm command; the generation rule of the abnormal sulfur rise rate alarm command includes: comparing the sulfur rise rate corresponding to each primary air duct with a preset sulfur rise standard rate; if the sulfur rise rate corresponding to the primary air duct is greater than the preset sulfur rise standard rate, then an abnormal sulfur rise rate alarm command is generated for that primary air duct.

[0034] Specifically, during boiler combustion operation, if there is a sulfur rise rate V corresponding to the primary air duct... E1 V E2 ......V EnGreater than the preset sulfur content increase standard rate V stand If the sulfur content rise rate is abnormal, an alarm command will be generated for the primary air duct to provide an alarm for the operator to refer to, adjust the boiler combustion in a timely manner, and make reasonable coal blending.

[0035] In some embodiments of this example, the boiler combustion control command includes a sulfur content assessment coefficient abnormality alarm command; the generation rule of the sulfur content assessment coefficient abnormality alarm command includes: comparing the sulfur content assessment coefficient corresponding to each primary air duct with a preset sulfur content standard value; if the sulfur content assessment coefficient corresponding to the primary air duct is greater than the preset sulfur content standard value, then a sulfur content assessment coefficient abnormality alarm command is generated for that primary air duct.

[0036] Specifically, during boiler combustion operation, if there is a sulfur content assessment coefficient S corresponding to the primary air duct... En Greater than the preset sulfur content standard value S stand If the sulfur content assessment coefficient is abnormal, an alarm command will be generated for the primary air duct to provide an alarm for abnormal data, which will be used as a reference for operators and as a basis for unit maintenance. The water-cooled wall around the burner corresponding to the primary air duct will be regarded as a key maintenance area for subsequent inspection.

[0037] In the above implementation process, this method performs online sampling of pulverized coal in each primary air duct and conducts real-time coal quality testing on the sampled pulverized coal to obtain the coal quality test results. By periodically statistically analyzing the sulfur content data of pulverized coal in the coal quality test results of each primary air duct, the corresponding sulfur content assessment coefficient and the corresponding sulfur content rise rate are obtained to guide boiler coal blending and maintenance. A correction algorithm is used to process the calorific value data of pulverized coal in the coal quality test results of each primary air duct to obtain the corresponding coal feed correction coefficient, which is used to guide boiler combustion optimization and adjustment. Based on the corresponding coal feed correction coefficient, sulfur content assessment coefficient, and sulfur content rise rate of each primary air duct, boiler combustion control commands are generated and executed to achieve the purpose of boiler coal blending and combustion adjustment. This method achieves the effects of online pulverized coal sampling, online testing, and online boiler combustion adjustment, avoiding the lag caused by traditional sampling and measurement, thereby achieving the goal of matching fuel supply with boiler heat load demand, and significantly improving the unit's response capability to AGC load commands throughout the full load range, especially under deep adjustment conditions.

[0038] Figure 3 This is a block diagram of a boiler combustion control system provided in one embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides a boiler combustion control system, comprising: The coal powder sampling and testing module is used to take online coal powder samples in each primary air duct, and to conduct coal quality testing on the online coal powder samples to obtain coal quality test results; among which, the coal quality test results include coal powder sulfur content data and coal powder calorific value data; The coal powder sulfur content data statistics module is used to perform periodic statistics on coal powder sulfur content data based on each primary air duct, and obtain the corresponding sulfur content evaluation coefficient and the corresponding sulfur content rise rate. The pulverized coal calorific value data processing module is used to process the pulverized coal calorific value data of each primary air duct based on the correction algorithm to obtain the corresponding coal feed correction coefficient. The boiler combustion control command generation module is used to generate and execute boiler combustion control commands based on the coal feed correction coefficient, sulfur content assessment coefficient, and sulfur content rise rate of each primary air duct.

[0039] Specifically, the system performs online sampling of pulverized coal in each primary air duct and conducts real-time coal quality testing on the sampled pulverized coal to obtain the test results. By periodically statistically analyzing the sulfur content data of pulverized coal in the coal quality test results of each primary air duct, the corresponding sulfur content assessment coefficient and the corresponding sulfur content rise rate are obtained to guide boiler coal blending and maintenance. A correction algorithm is used to process the calorific value data of pulverized coal in the coal quality test results of each primary air duct to obtain the corresponding coal feed correction coefficient, which is used to guide boiler combustion optimization and adjustment. Based on the corresponding coal feed correction coefficient, sulfur content assessment coefficient, and sulfur content rise rate of each primary air duct, boiler combustion control commands are generated and executed to achieve the purpose of boiler coal blending and combustion adjustment. This system achieves the effects of online pulverized coal sampling, online testing, and online boiler combustion adjustment, avoiding the lag caused by traditional sampling and measurement, thereby achieving the goal of matching fuel supply with boiler heat load demand, and significantly improving the unit's response capability to AGC load commands throughout the full load range, especially under deep adjustment conditions.

[0040] The present invention also provides a machine-readable storage medium storing instructions that, when executed by a processor 100, configure the processor 100 to perform the above-described boiler combustion control method.

[0041] Machine-readable storage media include both permanent and non-permanent, removable and non-removable media, which can store information by 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.

[0042] The present invention also provides an electronic device 10, which includes a memory 101, a processor 100, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, it implements the above-mentioned boiler combustion control method.

[0043] like Figure 4 The diagram shown is a schematic representation of an electronic device according to an embodiment of the present invention. Figure 4 As shown, the electronic device 10 of this embodiment includes a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, it implements the steps in the method embodiment described above. Alternatively, when the processor 100 executes the computer program 102, it implements the functions of each module / unit in the device embodiment described above.

[0044] For example, computer program 102 can be divided into one or more modules / units, one or more of which are stored in memory 101 and executed by processor 100 to complete the present invention. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of computer program 102 in electronic device 10. For example, computer program 102 can be divided into a pulverized coal sampling and detection module, a pulverized coal sulfur content data statistics module, a pulverized coal calorific value data processing module, and a boiler combustion control instruction generation module.

[0045] Electronic device 10 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Electronic device 10 may include, but is not limited to, processor 100 and memory 101. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 10 and does not constitute a limitation on electronic device 10. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0046] The processor 100 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0047] The memory 101 can be an internal storage unit of the electronic device 10, such as a hard disk or RAM of the electronic device 10. The memory 101 can also be an external storage device of the electronic device 10, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, FlashCard, etc., equipped on the electronic device 10. Furthermore, the memory 101 can include both internal and external storage units of the electronic device 10. The memory 101 is used to store computer programs and other programs and data required by the electronic device 10. The memory 101 can also be used to temporarily store data that has been output or will be output.

[0048] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0049] Those skilled in the art will understand that embodiments of this application can be provided as a method, system, or computer program 102 product. 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 102 product implemented 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.

[0050] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program 102 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 102 instructions. These computer program 102 instructions can be provided to a processor 100 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 100 of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0051] These computer program 102 instructions may also be stored in a computer-readable storage medium 101 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 101 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.

[0052] These computer program 102 instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus 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.

[0053] 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.

[0054] 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 boiler combustion control method, characterized in that, include: Coal powder was sampled online in each primary air duct, and the sampled coal powder was subjected to coal quality testing to obtain coal quality test results; wherein, the coal quality test results include coal powder sulfur content data and coal powder calorific value data; Periodic statistics were performed on the sulfur content data of pulverized coal in each primary air duct to obtain the corresponding sulfur content assessment coefficient and the corresponding sulfur content increase rate. The calorific value data of pulverized coal in each primary air duct is processed based on the correction algorithm to obtain the corresponding coal feed correction coefficient; wherein, the correction algorithm includes the mean algorithm and the standard deviation algorithm; Based on the coal feed correction coefficient, sulfur content assessment coefficient, and sulfur content rise rate of each primary air duct, boiler combustion control commands are generated and executed; wherein, the boiler combustion control commands include fuel control commands. The rules for generating the fuel control commands include: The coal feed correction coefficient corresponding to each primary air duct is imported into the coal feed feedback monitoring loop of the corresponding combustion layer coal feeder to perform a multiplication operation on the measured coal feed Fn to obtain the coal feed feedback value Fnr of the corresponding combustion layer coal feeder; where n = positive integer; The total boiler coal feed rate Fr is obtained by summing the coal feeder feedback values ​​of all combustion chamber coal feeders; the summation formula is as follows: ； The total coal feed rate Fr of the boiler is used as the feedback quantity in the fuel control loop to generate fuel control commands. The periodic statistical analysis of pulverized coal sulfur content data based on each primary air duct yields the corresponding sulfur content assessment coefficient and the corresponding sulfur content increase rate, including: Periodic statistics were performed on the sulfur content data of pulverized coal in each primary air duct to obtain the corresponding data lake; Calculate the mean and standard deviation of the data lake corresponding to each primary air duct; Based on the time series characteristics of the data lake, predict the sulfur content of pulverized coal; Based on the average value, standard deviation, and sulfur content prediction results of the data lake corresponding to each primary air duct, the sulfur content assessment coefficient and sulfur content rise rate corresponding to each primary air duct are obtained. The process of processing the pulverized coal calorific value data of each primary air duct based on the correction algorithm to obtain the corresponding coal feed correction coefficient includes: The calorific value data of pulverized coal for each primary air duct are processed using the mean algorithm and the standard deviation algorithm to obtain the coal feed correction coefficient for each primary air duct.

2. The boiler combustion control method according to claim 1, characterized in that, The boiler combustion control commands include an alarm command for abnormal sulfur rise rate. The generation rules for the abnormal sulfur content rise rate alarm command include: The sulfur rise rate corresponding to each primary air duct is compared with the preset sulfur rise standard rate. If the sulfur content rise rate of the primary air duct is greater than the preset sulfur content rise standard rate, an abnormal sulfur content rise rate alarm command will be generated for that primary air duct.

3. The boiler combustion control method according to claim 1, characterized in that, The boiler combustion control commands include an alarm command for abnormal sulfur content assessment coefficient. The generation rules for the sulfur content assessment coefficient abnormal alarm command include: The sulfur content assessment coefficients for each primary air duct are compared with the preset sulfur content standard values. If the sulfur content assessment coefficient of a primary air duct is greater than the preset sulfur content standard value, an abnormal sulfur content assessment coefficient alarm command will be generated for that primary air duct.

4. The boiler combustion control method according to claim 1, characterized in that, The prediction of pulverized coal sulfur content based on the time series characteristics of the data lake includes: Based on the time series characteristics of the data lake, a prediction model for sulfur content in pulverized coal was established and trained. The sulfur content of pulverized coal is predicted using a trained pulverized coal sulfur content prediction model.

5. A boiler combustion control system, characterized in that, The boiler combustion control method for performing any one of claims 1 to 4 includes: The coal powder sampling and testing module is used to perform online coal powder sampling in each primary air duct, and to perform coal quality testing on the online sampled coal powder to obtain coal quality testing results; wherein, the coal quality testing results include coal powder sulfur content data and coal powder calorific value data; The coal powder sulfur content data statistics module is used to perform periodic statistics on coal powder sulfur content data based on each primary air duct, and obtain the corresponding sulfur content evaluation coefficient and the corresponding sulfur content rise rate. The pulverized coal calorific value data processing module is used to process the pulverized coal calorific value data of each primary air duct based on the correction algorithm to obtain the corresponding coal feed correction coefficient. The boiler combustion control command generation module is used to generate and execute boiler combustion control commands based on the coal feed correction coefficient, sulfur content assessment coefficient, and sulfur content rise rate of each primary air duct.

6. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, the instruction causes the processor to be configured to perform the boiler combustion control method as described in any one of claims 1 to 4.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the boiler combustion control method according to any one of claims 1 to 4.

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