Combustion optimizing and adjusting system for coal-fired power plant

Abstract

The invention belongs to the technical field of coal-fired power plants, and particularly relates to a coal-fired power plant combustion optimization adjusting system which comprises an acquisition module, a comparison module, an analysis module and an adjusting module, the acquisition module is connected with the comparison module, the comparison module is connected with the analysis module, and the analysis module is connected with the adjusting module; the acquisition module is used for acquiring equipment parameters and environmental parameters of a coal-fired power plant, acquiring quality parameters of fire coal, and establishing a load state coefficient after normalization; the comparison module is used for comparing the load state coefficient with data in a database; the analysis module is used for analyzing and processing the compared data; the adjusting module is used for optimizing and adjusting combustion according to the analysis result, and the collected equipment parameters comprise a boiler wind speed index and a temperature index of the coal-fired power plant; the collected environmental parameters comprise a temperature difference span index. Different combustion schemes can be allocated at any time according to conditions, and the optimal fire coal combustion efficiency can be achieved.

Description

Technical Field

[0001] This invention belongs to the technical field of coal-fired power plants, specifically relating to a combustion optimization and regulation system for coal-fired power plants. Background Technology

[0002] Large-scale coal-fired power plants, which primarily use coal as fuel, have specific requirements for coal quality and power generation efficiency. For example, boilers in thermal power plants need to select corresponding design coal types. When the coal quality exceeds the set adaptability range, it will significantly impact the boiler's safety and economy. In actual operation, problems such as coking in the boiler furnace, overheating, tube rupture, and flameout are prominent due to large variations in coal quality. To ensure the safe and economical operation of the unit, coal blending is currently the safest and most economical method for stabilizing coal quality in thermal power plants. This involves mixing different coal types in a certain proportion according to certain principles before pulverized coal enters the furnace, forming a coal blend to meet the boiler's operational requirements.

[0003] Current coal-fired power plant combustion requires more human intervention, resulting in low efficiency and a low level of intelligence in coal combustion methods. Furthermore, current coal combustion methods often focus on combustion itself, without unified coordination of coal quality inspection and coal yard management. This often leads to situations where the combustion method is theoretically feasible, but in actual operation, due to the lack of strict inspection of coal quality and strict management of coal quantity and quality, combustion tests are required before each blending, resulting in very low efficiency. At the same time, due to the difficulty in determining the scheme, it is impossible to adjust different combustion schemes according to the situation, thus failing to achieve the optimal coal combustion efficiency.

[0004] Therefore, a combustion optimization and regulation system for coal-fired power plants is needed to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a combustion optimization and regulation system for coal-fired power plants to solve the problems mentioned in the background art.

[0006] The present invention achieves the above objectives through the following technical solutions:

[0007] A combustion optimization and regulation system for a coal-fired power plant, comprising:

[0008] The system includes a data acquisition module, a comparison module, an analysis module, and an adjustment module. The data acquisition module is connected to the comparison module, the comparison module is connected to the analysis module, and the analysis module is connected to the adjustment module.

[0009] The data acquisition module is used to collect equipment and environmental parameters of coal-fired power plants, as well as coal quality parameters, and establish load state coefficients after normalization.

[0010] The comparison module is used to compare the load status coefficient with the data in the database;

[0011] The analysis module is used to analyze and process the compared data;

[0012] The adjustment module is used to optimize and adjust combustion based on the analysis results.

[0013] A further improvement lies in the fact that, in the acquisition module,

[0014] The collected equipment parameters include the wind speed index and temperature index of coal-fired power plant boilers;

[0015] The collected environmental parameters include the temperature range index;

[0016] The collected quality parameters include the particle size index of the coal.

[0017] The wind speed index of coal-fired power plant boilers is labeled as FS, and the temperature index of coal-fired power plant boilers is labeled as WD.

[0018] The temperature difference span index is denoted as KD;

[0019] The particle size index is labeled KL;

[0020] The load state coefficient is established by normalizing the boiler wind speed index, boiler temperature index, temperature difference span index, and particle size index of coal-fired power plants.

[0021]

[0022] In the formula, PG is the load state coefficient, and k1, k2, k3, and k4 are the proportional coefficients of the wind speed index, temperature index, temperature difference span index, and particle size index of the coal-fired power plant boiler, respectively, and k1, k2, k3, and k4 > 0.

[0023] A further improvement is to compare the load state coefficient with the data in the database, obtain the first threshold and the second threshold of the load state coefficient in the database in advance, compare the load state coefficient with the first threshold and the second threshold, and if the second threshold is greater than the first threshold, mark the first threshold as PG′ and the second threshold as PG″.

[0024] If the load condition coefficient is greater than the second threshold of the load condition coefficient, it indicates that the coal-fired power plant is in good combustion condition and is marked as a qualified object;

[0025] If the first threshold of the load state coefficient is less than or equal to the second threshold of the load state coefficient, it indicates that the combustion state of the coal-fired power plant is poor and it is marked as a key object. The number of key objects is calculated and output, and a first sorting table is generated according to the load state coefficient from largest to smallest.

[0026] If the load state coefficient is less than the first threshold of the load state coefficient, it indicates that the combustion state of the coal-fired power plant has not met the standard, and it is marked as an over-priority object. The number of over-priority objects is calculated and output, and a second sorting table is generated according to the load state coefficient from largest to smallest.

[0027] A further improvement is to pre-set the first sorting table data in the database, compare the similarity between the generated first sorting table and the first sorting table data in the database, and extract the top five data with the highest similarity.

[0028] A second sorting table is pre-defined in the database. The generated second sorting table is then compared with the second sorting table in the database to extract the top five most similar data.

[0029] A further improvement involves analyzing the top five most similar data points, taking the average of the similarity data, and using the magnitude of the average value to determine the boiler's condition or the quality of the coal.

[0030] A further improvement is that when the status of the corresponding boiler or the quality of the coal is abnormal, a corresponding warning will be issued.

[0031] A further improvement is that when the boiler malfunctions, the wind speed or temperature of the coal-fired power plant boiler can be optimized and adjusted.

[0032] A further improvement is that when the quality of the coal is abnormal, the coal will be tested and the substandard products will be removed.

[0033] The beneficial effects of the present invention are:

[0034] (1) By collecting equipment parameters, environmental parameters and quality parameters, a load state coefficient is established. The load state coefficient is compared with the first threshold and the second threshold of the load state coefficient, which facilitates the quantitative assessment of the health of the boiler and coal, timely detection and elimination of potential problems and faults, and reduction of the failure rate and accident risk of the boiler and coal, thereby improving the reliability and safety of the boiler and coal.

[0035] (2) Pre-set the first sorting table data in the database, compare the similarity between the generated first sorting table and the first sorting table data in the database, and extract the top five similar data. Pre-set the second sorting table data in the database, compare the similarity between the generated second sorting table and the second sorting table data in the database, and extract the top five similar data. Analyze the extracted top five similar data, take the average value of the similarity data, and judge the state of the boiler or the quality of the coal based on the size of the average value. Optimize and adjust the combustion based on the actual results. Different combustion schemes can be adjusted at any time according to the situation to achieve the best coal combustion efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is an overall block diagram of a combustion optimization and regulation system for coal-fired power plants proposed in this invention. Detailed Implementation

[0037] The present invention will now be described in further detail with reference to the accompanying drawings. It should be noted that the following specific embodiments are only used to further illustrate the present invention and should not be construed as limiting the scope of protection of the present invention. Those skilled in the art can make some non-essential improvements and adjustments to the present invention based on the above-described contents.

[0038] Example 1

[0039] like Figure 1 As shown, the present invention provides a combustion optimization and control system for a coal-fired power plant, comprising:

[0040] The system includes a data acquisition module, a comparison module, an analysis module, and an adjustment module. The data acquisition module is connected to the comparison module, the comparison module is connected to the analysis module, and the analysis module is connected to the adjustment module.

[0041] The data acquisition module is used to collect equipment and environmental parameters of coal-fired power plants, as well as coal quality parameters, and establish load state coefficients after normalization.

[0042] The comparison module is used to compare the load status coefficient with the data in the database;

[0043] The analysis module is used to analyze and process the compared data;

[0044] The adjustment module is used to optimize and adjust combustion based on the analysis results.

[0045] Furthermore, in the acquisition module,

[0046] The collected equipment parameters include the wind speed index and temperature index of coal-fired power plant boilers;

[0047] The collected environmental parameters include the temperature range index;

[0048] The collected quality parameters include the particle size index of the coal.

[0049] The wind speed index of coal-fired power plant boilers is labeled as FS, and the temperature index of coal-fired power plant boilers is labeled as WD.

[0050] The temperature difference span index is denoted as KD;

[0051] The particle size index is labeled KL;

[0052] The load state coefficient is established by normalizing the boiler wind speed index, boiler temperature index, temperature difference span index, and particle size index of coal-fired power plants.

[0053]

[0054] In the formula, PG is the load state coefficient, and k1, k2, k3, and k4 are the proportional coefficients of the wind speed index, temperature index, temperature difference span index, and particle size index of the coal-fired power plant boiler, respectively, and k1, k2, k3, and k4 > 0.

[0055] Furthermore, the load state coefficient is compared with the data in the database. The first threshold and the second threshold of the load state coefficient in the database are obtained in advance. The load state coefficient is compared with the first threshold and the second threshold. If the second threshold is greater than the first threshold, the first threshold is marked as PG′ and the second threshold is marked as PG″.

[0056] If the load condition coefficient is greater than the second threshold of the load condition coefficient, it indicates that the coal-fired power plant is in good combustion condition and is marked as a qualified object;

[0057] If the first threshold of the load state coefficient is less than or equal to the second threshold of the load state coefficient, it indicates that the combustion state of the coal-fired power plant is poor and it is marked as a key object. The number of key objects is calculated and output, and a first sorting table is generated according to the load state coefficient from largest to smallest.

[0058] If the load state coefficient is less than the first threshold of the load state coefficient, it indicates that the combustion state of the coal-fired power plant has not met the standard, and it is marked as an over-priority object. The number of over-priority objects is calculated and output, and a second sorting table is generated according to the load state coefficient from largest to smallest.

[0059] It should be added that by comparing the load state coefficient with the first and second thresholds of the load state coefficient, the health status of the boiler and coal can be easily quantitatively assessed, potential problems and faults can be identified and eliminated in a timely manner, the failure rate and accident risk of the boiler and coal can be reduced, thereby improving the reliability and safety of the boiler and coal.

[0060] Furthermore, a first sorting table of data in the database is pre-defined, and the generated first sorting table is compared with the first sorting table of data in the database to extract the top five data with the highest similarity.

[0061] A second sorting table is pre-defined in the database. The generated second sorting table is then compared with the second sorting table in the database to extract the top five most similar data.

[0062] It should be added that this similarity comparison improves the efficiency of detection.

[0063] Furthermore, the top five similarity data will be extracted and analyzed, the average similarity data will be taken, and the boiler status or coal quality will be judged based on the magnitude of the average value.

[0064] Furthermore, when the status of the corresponding boiler or the quality of the coal becomes abnormal, a corresponding warning will be issued.

[0065] Furthermore, when the boiler malfunctions, the wind speed or temperature of the coal-fired power plant boiler is optimized and adjusted.

[0066] Furthermore, when the quality of the coal is abnormal, the coal is tested and the substandard products are removed.

[0067] It should be added that this allows for rapid assessment of the boiler's condition or the quality of the coal in case of anomalies. When the boiler's condition is abnormal, the boiler's airflow or temperature can be optimized and adjusted. When the coal quality is abnormal, the coal can be tested and defective products can be removed. Different combustion schemes can be adjusted as needed to achieve optimal coal combustion efficiency.

[0068] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0069] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0070] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0071] In addition, the functional modules in the various embodiments of this application 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.

[0072] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in the embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0073] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A combustion optimization and regulation system for a coal-fired power plant, characterized in that: include: A collection module, a comparison module, an analysis module and an adjustment module, wherein the collection module is connected to the comparison module, the comparison module is connected to the analysis module, and the analysis module is connected to the adjustment module; The acquisition module is used to collect equipment parameters and environmental parameters of coal-fired power plants, as well as coal quality parameters, and establish load state coefficients after normalization; The comparison module is used to compare the load state coefficient with the data in the database; The analysis module is used to analyze and process the compared data; The adjustment module is used to optimize the combustion according to the analysis results.

2. A combustion optimization and regulation system for a coal-fired power plant according to claim 1, characterized in that: In the acquisition module, The equipment parameters collected include wind speed index and temperature index of coal-fired power plant boilers; The environmental parameters collected include temperature span index; The quality parameters collected include the particle size index of the coal being burned; The wind speed index of the coal-fired power plant boiler is marked as FS, and the temperature index of the coal-fired power plant boiler is marked as WD; The temperature difference span index is labeled as KD; The granularity index is labeled KL; The load state coefficient is established by normalizing the wind speed index, temperature index, temperature difference span index and particle size index of the coal-fired power plant boiler: Where PG is the load state coefficient, k1, k2, k3, and k4 are the proportional coefficients of the coal-fired power plant boiler wind speed index, coal-fired power plant boiler temperature index, temperature difference span index, and particle size index, respectively, and k1, k2, k3, and k4>0.

3. A combustion optimization and regulation system for a coal-fired power plant according to claim 2, characterized in that: Compare the load state coefficient with the data in the database, pre-acquire the first threshold value of the load state coefficient and the second threshold value of the load state coefficient in the database, compare the load state coefficient with the first threshold value of the load state coefficient and the second threshold value of the load state coefficient, and if the second threshold value of the load state coefficient is greater than the first threshold value of the load state coefficient, mark the first threshold value of the load state coefficient as PG′ and mark the second threshold value of the load state coefficient as PG″; If the load state coefficient is greater than the second threshold value of the load state coefficient, it indicates that the combustion state of the coal-fired power plant is good and is marked as a qualified object; If the first threshold value of the load state coefficient is ≤ the load state coefficient ≤ the second threshold value of the load state coefficient, it indicates that the combustion state of the coal-fired power plant is poor, and it is marked as a key object, the number of key objects is calculated and output, and a first sorting table is generated from large to small according to the load state coefficient; If the load state coefficient is less than the first threshold value of the load state coefficient, it indicates that the combustion state of the coal-fired power plant does not meet the standard and is marked as an ultra-key object. The number of ultra-key objects is calculated and output, and a second sorting table is generated from large to small according to the load state coefficient.

4. A combustion optimization and regulation system for a coal-fired power plant according to claim 3, characterized in that: Preset the first sorting table data in the database, compare the generated first sorting table with the first sorting table data in the database for similarity, and extract the top five similarity data; The second sorting table data in the database is preset, and the generated second sorting table is compared with the second sorting table data in the database for similarity, and the top five similarity data are extracted.

5. A combustion optimization and regulation system for a coal-fired power plant according to claim 4, characterized in that: The top five similarity data are extracted for analysis, the average value of the similarity data is taken, and the state of the boiler or the quality of the coal is judged based on the size of the average value.

6. A combustion optimization and regulation system for a coal-fired power plant according to claim 5, characterized in that: When the status of the corresponding boiler or the quality of the coal is abnormal, a corresponding warning will be issued.

7. A combustion optimization and regulation system for a coal-fired power plant according to claim 6, characterized in that: When the boiler status is abnormal, the wind speed or temperature of the coal-fired power plant boiler is optimized and adjusted.

8. A combustion optimization and regulation system for a coal-fired power plant according to claim 6, characterized in that: When the quality of the coal is abnormal, the coal is tested and unqualified products are eliminated.

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