Real-time monitoring information system based on thermal power plant and control method

Abstract

The invention discloses a real-time monitoring information system based on a thermal power plant and a control method, and relates to the technical field of thermal power plants, accurate operation control is realized through modular design, an as-fired coal quality data acquisition module synchronously acquires data of elements, minerals, moisture and volatile components by relying on LIBS, terahertz and microwave resonant cavities and other acquisition equipment, and the real-time monitoring information system and control method are applied to the thermal power plant. Calculating a coal quality evaluation value through an algorithm; a boiler combustion feed-forward control module formulates a graded combustion strategy according to the combustion parameters; the biomass blending combustion characteristic acquisition module acquires combustion, chemical and ash form indexes at a specific time point after a combustion strategy is adjusted, and analyzes a slagging risk assessment value in combination with a weight model; the blending combustion regulation and control strategy analysis module dynamically optimizes the biomass blending combustion proportion according to the slagging risk grade, and coordinated regulation such as air distribution and soot blowing is assisted. The system realizes coal quality-combustion-slagging full-chain data driving decision, improves the combustion efficiency and safety of a thermal power plant, and reduces the slagging risk and pollutant emission.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal power plants, and in particular to a real-time monitoring information system and a control method based on thermal power plants. Background Art

[0002] With the development of the power industry, thermal power plants continue to play a vital role in energy supply. However, variations in the quality of incoming coal significantly impact boiler combustion and equipment operation. Different coal qualities vary in properties such as calorific value, volatile matter, and ash content, directly impacting combustion efficiency and slagging tendency. Therefore, accurately acquiring and evaluating incoming coal quality data is crucial for optimizing boiler combustion control.

[0003] Prior art, such as the invention patent application with publication number CN113741359A, discloses a safety monitoring system and method for a thermal power plant under a mixed coal combustion mode. The system includes: a data acquisition subsystem for real-time acquisition of boiler operating data; a data processing subsystem for cleaning and processing the real-time acquired boiler operating data to obtain effective boiler equipment operating data; a safety analysis subsystem for comprehensive equipment parameter evaluation, boiler load capacity evaluation, slagging feedback analysis, and operating condition evaluation; a data storage subsystem for storing boiler equipment operating data and analysis data output by the safety analysis subsystem; and an alarm triggering subsystem for determining and outputting corresponding control signals to an alarm device based on the analysis data output by the safety analysis subsystem and the set abnormal alarm logic conditions. Compared with the prior art, the present invention can accurately monitor the entire mixed coal combustion process in real time, conduct safety analysis, and provide feedback to optimize operating parameters to ensure safe and stable boiler operation.

[0004] Regarding the above-mentioned solution, this applicant has identified at least the following technical issues: 1. Existing technologies may rely on a single detection method for coal quality testing, failing to achieve multimodal sensor fusion. Testing is time-consuming, with traditional methods requiring 4-6 hours to complete coal element, mineral, and moisture / volatile matter testing. This makes it difficult to meet the real-time coal quality monitoring needs of thermal power plants, preventing them from adjusting combustion strategies in response to changes in coal quality, impacting power generation efficiency and energy utilization.

[0005] 2. Existing technologies present significant difficulties in real-time monitoring of ash morphology and composition in high-temperature environments. This may lack an effective combination of monitoring equipment, resulting in an inability to accurately and real-time obtain relevant information. This leads to low slagging warning accuracy, making it difficult to effectively and effectively warn of slagging risks in advance and ensure safe and stable boiler operation.

[0006] 3. Existing technologies lack a fully coupled model for coal quality, combustion, and slagging, making it impossible to implement dynamic feedforward control based on slagging risk. When faced with changes in coal quality and slagging risks, existing technologies may not be able to quickly adjust the blending ratio, nor can they coordinately optimize strategies such as air distribution and soot blowing. This makes it difficult to maintain an optimal combustion process, increasing energy consumption and the risk of equipment failure.

[0007] 3. Existing technologies may not be able to effectively integrate and analyze multiple parameters, making it difficult to achieve intelligent decision support. Furthermore, they lack an integrated system architecture like this technology. Poor coordination between different devices and systems prevents real-time data sharing and interaction, making it difficult to establish a comprehensive and efficient monitoring and management system, impacting the overall operational efficiency and safety of thermal power plants. Summary of the Invention

[0008] In view of the above-mentioned technical deficiencies, the object of the present invention is to provide a real-time monitoring information system and control method based on a thermal power plant.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a real-time monitoring information system based on a thermal power plant, including: a furnace coal quality data acquisition module: used to install collection equipment in each boiler of the target thermal power plant, so as to obtain the coal quality data corresponding to each boiler, and then analyze and obtain the coal quality evaluation value corresponding to each boiler.

[0010] Boiler combustion feedforward control module: used to analyze the combustion feedforward control strategy corresponding to each boiler based on the coal quality evaluation value corresponding to each boiler.

[0011] Biomass blending characteristics acquisition module: It is used to set several biomass collection time points after each boiler is adjusted according to the corresponding combustion feedforward control strategy, so as to collect the combustion property index, chemical property index and ash morphology index corresponding to each boiler at each biomass collection time point, and then analyze the slagging risk assessment value corresponding to each boiler at each biomass collection time point.

[0012] Blending control strategy analysis module: used to analyze the blending control strategy corresponding to each boiler at each biomass collection time point based on the slagging risk assessment value corresponding to each boiler at each biomass collection time point.

[0013] In a second aspect, the present invention provides a real-time monitoring information control method based on a thermal power plant, comprising: step one, obtaining the quality data of the coal entering the furnace: installing collection equipment in each boiler of the target thermal power plant to obtain the coal quality data corresponding to each boiler, and then analyzing and obtaining the coal quality evaluation value corresponding to each boiler.

[0014] Step 2: Boiler combustion feedforward control: Based on the coal quality assessment value corresponding to each boiler, the combustion feedforward control strategy corresponding to each boiler is analyzed.

[0015] Step 3. Obtaining the biomass co-combustion characteristics: After each boiler is adjusted according to the corresponding combustion feedforward control strategy, several biomass collection time points are set to collect the combustion property index, chemical property index and ash morphology index corresponding to each boiler at each biomass collection time point, and then analyze the slagging risk assessment value corresponding to each boiler at each biomass collection time point.

[0016] Step 4: Analysis of the co-firing control strategy: Based on the slagging risk assessment value corresponding to each boiler at each biomass collection time point, the co-firing control strategy corresponding to each boiler at each biomass collection time point is analyzed.

[0017] The beneficial effects of the present invention are: 1. In the embodiment of the present invention, through the integration of multimodal sensor fusion and dynamic feedforward control system, LIBS, terahertz, and microwave resonant cavity are deployed in a coordinated manner in multimodal sensor fusion to achieve synchronous detection of coal elements, minerals, moisture and volatility at the sub-second level. The detection speed is greatly improved compared with traditional methods. At the same time, a combination of high-temperature endoscope and XRF in-situ probe is used to overcome the problem of real-time monitoring of ash morphology and composition under high-temperature environment; in the dynamic feedforward control system, a full-chain coupling model of coal quality-combustion-slagging is constructed, and the blending ratio is dynamically adjusted based on the slagging risk assessment value, and the air distribution and soot blowing strategies can be coordinated and optimized.

[0018] 2. This embodiment of the present invention installs a LIBS module, a terahertz module, and a microwave resonant cavity in the pulverizer outlet pipe to collect data on the incoming coal quality from different angles. The LIBS module measures the content of various elements, the terahertz module determines the content of various minerals, and the microwave resonant cavity measures moisture and volatile matter. This multi-module collaborative approach comprehensively and accurately reflects all coal quality characteristics, significantly improving monitoring accuracy compared to traditional single-unit coal quality testing methods. Based on the coal quality assessment values ​​corresponding to each boiler, the system analyzes the boiler's combustion feedforward control strategy. Detailed control strategies are developed for different coal quality levels, including primary air volume regulation, pulverizer speed control, combustion optimization instructions, NOx control, dynamic oxygen control, and soot blowing strategies. This targeted control strategy enables the boiler to achieve efficient and stable combustion under varying coal quality conditions, improving combustion efficiency and reducing pollutant emissions. This dynamic adjustment mechanism adapts to real-time changes in coal quality, ensuring the boiler is always in optimal operating condition and minimizing the impact of coal quality fluctuations on boiler operation.

[0019] 3. In the embodiment of the present invention, by collecting the combustion property index, chemical property index and ash morphology index corresponding to each boiler at each biomass collection time point, a comprehensive analysis is performed to obtain a slagging risk assessment value. This assessment method that comprehensively considers multiple factors can more accurately reflect the slagging risk status of the boiler and provide a reliable basis for formulating reasonable co-combustion control strategies. According to the slagging risk level, the system formulates different co-combustion control strategies. For low-risk situations, the biomass co-combustion ratio is appropriately increased to make full use of biomass energy; for medium-risk situations, the biomass co-combustion ratio is reduced, and corresponding adjustment measures are taken at the same time; for high-risk situations, the biomass co-combustion ratio is greatly reduced or even the special biomass co-combustion is suspended, and emergency treatment measures are taken, such as high-pressure water washing and continuous purging. These targeted control strategies can effectively reduce the slagging risk of the boiler and ensure the safe operation of the boiler.

[0020] 4. This embodiment of the present invention analyzes and makes decisions based on large amounts of real-time data. By establishing various assessment models and calculation formulas, it automates functions such as coal quality assessment, combustion feedforward control strategy analysis, slagging risk assessment, and co-combustion control strategy analysis. This data-driven decision-making approach reduces manual intervention and improves the accuracy and timeliness of decisions. The system integrates multiple modules, including coal quality data acquisition, boiler combustion feedforward control, biomass co-combustion characteristics acquisition, and co-combustion control strategy analysis, to achieve comprehensive monitoring and management of thermal power plant boiler operations. This integrated management approach improves the system's overall operational efficiency and reduces management costs. Improved energy efficiency: By optimizing boiler combustion control and rationally adjusting the biomass co-combustion ratio, the system can improve the energy efficiency of thermal power plants, reduce coal consumption, and lower power generation costs. Furthermore, fully utilizing biomass energy contributes to energy diversification and enhances energy supply security. Furthermore, while optimizing the combustion process, the system can effectively control emissions of pollutants such as NOx. By adjusting the SNCR system urea solution injection amount and the SCR system ammonia injection amount according to coal quality and combustion conditions, the emission concentration of pollutants is reduced, meeting environmental protection requirements and reducing pollution to the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 This is a schematic diagram of the system module connection of the present invention.

[0023] Figure 2The present invention is a flowchart of the steps for implementing the method. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0025] The present invention is implemented as follows Figure 1 As shown in FIG, the real-time monitoring information system based on the thermal power plant includes: a coal quality data acquisition module, a boiler combustion feedforward control module, a biomass blending combustion characteristic acquisition module, a blending combustion control strategy analysis module and a database.

[0026] The boiler combustion feedforward control module is connected to the coal quality data acquisition module and the biomass blending combustion characteristic acquisition module, the biomass blending combustion characteristic acquisition module is connected to the blending combustion control strategy analysis module, and the blending combustion control strategy analysis module is connected to the database.

[0027] The module for acquiring coal quality data entering the furnace is used to install data collection equipment in each boiler of the target thermal power plant, thereby acquiring the coal quality data corresponding to each boiler, and then analyzing and obtaining the coal quality assessment value corresponding to each boiler.

[0028] In a specific embodiment, the data collection equipment is installed in each boiler of the target thermal power plant. The specific installation process is as follows: A1. LIBS module installation: On the outlet pipe of the coal mill corresponding to each boiler, a position 1.5 meters away from the coal mill outlet is located and the LIBS module is installed at this position. At the same time, a tilted mounting bracket is used to fix the LIBS module to the bracket. The angle of the bracket is adjusted so that the LIBS module is installed at a 30° tilt angle with the coal mill outlet pipe.

[0029] A2. Terahertz module installation: After completing the LIBS module installation, use the axis of the LIBS module as a reference and use an axis calibration instrument to ensure that the terahertz module installation position is coaxial with the LIBS module. Place the terahertz module on the platform and fine-tune the platform position and angle so that the central axis of the terahertz module coincides with the central axis of the LIBS module. At the same time, use the angle adjustment device to adjust the laser incident angle of the terahertz module to 5°. Then, use welding and riveting to fix the terahertz module to the outer wall of the pulverizer outlet pipe corresponding to each boiler.

[0030] A3. Installation of microwave resonant cavity: Before installing the microwave resonant cavity, first thoroughly clean the outer wall of the pulverized coal pipeline corresponding to each boiler to remove dust, oil stains, rust impurities on the surface, and use sandpaper to polish the installation part to make its surface flat and smooth. Then surround the annular microwave resonant cavity with an inner diameter of 50mm on the outer wall of the pulverized coal outlet pipeline corresponding to each boiler and fix it with the matching clamp or hoop.

[0031] In a specific embodiment, the analysis obtains the coal quality assessment value corresponding to each boiler, and the specific acquisition process is as follows: B1. Based on the LIBS module installed in each boiler, the content of each element corresponding to each boiler is obtained; based on the terahertz module installed in each boiler, the content of each mineral corresponding to each boiler is obtained; based on the microwave resonant cavity installed in each boiler, the moisture content and volatile matter content corresponding to each boiler are obtained.

[0032] It should be noted that laser-induced breakdown spectroscopy (LIBS) is based on the principles of atomic emission spectroscopy. When a high-energy laser pulse is focused onto the surface of a coal sample, the instantaneous high energy vaporizes and ionizes a small portion of the sample's surface material, forming a high-temperature, high-pressure plasma. As the plasma cools, the excited atoms and ions transition back to their ground state and emit light with specific wavelengths. These wavelengths correspond to characteristic spectral lines of different elements. By detecting and analyzing these characteristic spectral lines, the types and concentrations of elements in the coal can be determined. Terahertz waves are electromagnetic waves between microwaves and infrared, and many substances exhibit unique absorption and scattering properties in the terahertz frequency band. Different minerals absorb and scatter terahertz waves to varying degrees due to differences in their molecular structure and chemical bonding. Leveraging this characteristic, by measuring the signal changes following the interaction of terahertz waves with the coal sample, the mineral composition of the coal can be identified and quantitatively analyzed. A microwave resonant cavity is a device that resonates at a specific frequency. When pulverized coal containing moisture and volatiles enters a microwave resonant cavity, the polar molecules in these components rotate and vibrate under the influence of the microwave electric field, absorbing microwave energy. The amount of microwave energy absorbed varies depending on the moisture and volatile content, causing variations in parameters such as the microwave resonant cavity's resonant frequency and quality factor. By measuring these changes, the moisture and volatile content of the coal can be determined.

[0033] B2. Record the element content, mineral content, moisture content and volatile content of each boiler as 、 、 and ,in, Indicates the number corresponding to each boiler, , u is a positive integer, Indicates the number corresponding to each element, , is a positive integer, It is also a collection of elements. Indicates the number corresponding to each mineral, , is a positive integer, is the collection of all minerals, substitute into the calculation formula: The coal quality evaluation value corresponding to each boiler is obtained ,in, 、 、 and They are the maximum values ​​of the statistical data of each element content, the maximum values ​​of the statistical data of each mineral content, the maximum values ​​of the statistical data of the moisture content and the maximum values ​​of the statistical data of the volatile content in the historical period of the target thermal power plant. 、 、 、 are the weight factors corresponding to the set content of each element in the boiler, the weight factors corresponding to the content of each mineral, the weight factors corresponding to the moisture content, and the weight factors corresponding to the volatile content, and e is expressed as a natural constant.

[0034] It should be noted that Represents a multiplication sign.

[0035] It's important to note that factors related to coal quality, such as elemental content, mineral content, moisture content, and volatile matter content, each have different dimensions and orders of magnitude. Comparing these with historical maximum values ​​and then calculating them with the natural constant e can transform these data of varying dimensions onto a relatively uniform scale. For example, regardless of the factor, the calculated results will fluctuate within a certain range, facilitating comparison and comprehensive analysis, effectively eliminating the interference of dimensional differences in the evaluation results. Using historical maximum values ​​as a reference standard provides a common benchmark for coal quality data from different boilers and at different times. This is like evaluating all coal quality data from the same starting point, making the evaluation process more fair and objective, and the results more consistent and interpretable.

[0036] Boiler combustion feedforward control module: used to analyze the combustion feedforward control strategy corresponding to each boiler based on the coal quality evaluation value corresponding to each boiler.

[0037] In a specific embodiment, the combustion feedforward control strategy corresponding to each boiler is analyzed, and the specific analysis process is as follows: C1. Analyze the coal quality grade corresponding to each boiler, and the coal quality grades include high-quality coal, medium-quality coal and low-quality coal.

[0038] C2. If the coal quality grade corresponding to a boiler is high-quality coal, the primary air volume adjustment is as follows: the air volume is reduced by 3.2%. The pulverizer speed control is as follows: the target pulverized coal fineness is lowered to 12%, and the speed is increased by 2.4%. The combustion optimization instruction is as follows: the burnout damper opening is adjusted from 0.05 to 35%, the upper secondary air volume of the secondary air ratio is increased by 5%, and the urea solution injection amount of the SNCR system in NOx control is reduced by 15%.

[0039] C3. If the coal quality grade corresponding to a boiler is medium, the primary air volume adjustment is as follows: increase the air volume by 1.5%. The pulverizer speed control is as follows: reduce the target pulverized coal fineness to 10% and increase the speed by 1.5%. The combustion optimization instruction is as follows: adjust the burnout damper opening to 30%. The dynamic oxygen control is as follows: increase the oxygen setpoint to 3.3%. The soot blowing strategy is as follows: increase the soot blowing frequency to once every three hours.

[0040] C4. If the coal quality grade corresponding to a boiler is low-quality coal, the primary air volume adjustment is as follows: increase the air volume by 2.5%. For pulverizer speed control, reduce the target pulverized coal fineness to 8% and increase the speed by 1.2%. For combustion optimization instructions, adjust the burnout damper opening from 0.5% to 55%, increase the secondary air swirl intensity by 20%, increase the ammonia injection rate of the SCR system by 25%, increase the limestone slurry supply by 30%, lower the furnace temperature limit by 50°C, and increase the soot blowing frequency to once every two hours.

[0041] In a specific embodiment, the analysis of the coal quality grade corresponding to each boiler is carried out as follows: the coal quality evaluation value corresponding to each boiler is compared with the coal quality evaluation value interval corresponding to each set coal quality grade. If the coal quality evaluation value corresponding to a certain boiler is within the coal quality evaluation value interval corresponding to a set coal quality grade, the set coal quality grade is used as the coal quality grade corresponding to the boiler. In this way, the coal quality grade corresponding to each boiler is analyzed.

[0042] Biomass blending characteristics acquisition module: It is used to set several biomass collection time points after each boiler is adjusted according to the corresponding combustion feedforward control strategy, so as to collect the combustion property index, chemical property index and ash morphology index corresponding to each boiler at each biomass collection time point, and then analyze the slagging risk assessment value corresponding to each boiler at each biomass collection time point.

[0043] In a specific embodiment, the slagging risk assessment value corresponding to each boiler at each biomass collection time point is analyzed. The specific analysis process is as follows: the combustion property adaptation value, chemical property adaptation value and ash morphology adaptation value corresponding to each boiler at each biomass collection time point are analyzed and recorded as 、 and ,in, Indicates the number corresponding to each boiler, , u is a positive integer, Indicates the number corresponding to each biomass collection time point, , is a positive integer, It is also the sum of each biomass collection time point, substituted into the calculation formula: The slagging risk assessment value corresponding to each boiler at each biomass collection time point is obtained. ,in, 、 、 They are the standard combustion property adaptation value, standard chemical property adaptation value, and standard ash morphology adaptation value corresponding to the set boiler. 、 、 They are the weight factors corresponding to the set boiler combustion property adaptation value, the weight factors corresponding to the chemical property adaptation value, and the weight factors corresponding to the ash morphology adaptation value.

[0044] It should be noted that extensive historical operating data from the target thermal power plants was collected, covering adaptation values ​​for combustion properties, chemical properties, and ash morphology. Statistical analysis was performed on these parameters, and statistical quantities such as mean, median, and standard deviation were calculated for each parameter during periods of normal, stable boiler operation without significant slagging issues. Based on these statistical quantities, the central value within the range was taken as the standard value. A workshop was held with boiler operation experts, technical experts in thermal engineering, and researchers with extensive experience in slagging research within the thermal power plants. Drawing on their extensive theoretical knowledge and practical experience, these experts discussed and determined the relative importance of combustion properties, chemical properties, and ash morphology in slagging risk assessment. Based on years of field operation experience, some experts believe that under certain specific fuel conditions, chemical properties play a dominant role in determining slagging risk and should be given a higher weight. Other experts emphasize that under specific boiler structures and operating conditions, combustion properties are more critical. Through extensive discussion and comprehensive considerations among these experts, weighting factors for each factor were determined.

[0045] In a specific embodiment, the combustion property adaptation value, chemical property adaptation value and ash morphology adaptation value corresponding to each boiler at each biomass collection time point are analyzed, and the specific analysis process is as follows: D1. Obtain the combustion property index, chemical property index and ash morphology index corresponding to each boiler at each biomass collection time point, the combustion property index includes the flame length and flame deflection, the chemical property index includes the content and proportion of each ash component, and the ash morphology index includes the sphericity of ash particles, the porosity of ash particles and the ash bulk density.

[0046] It should be noted that the images are taken using a high-temperature endoscope or high-speed camera installed near the boiler furnace observation hole and the burner. The equipment captures flame images at a certain frame rate. Using image processing technology, the flame's start and end positions are identified in the captured images. Image measurement algorithms, such as pixel-based distance conversion, are then applied to determine the flame's spatial length and skewness.

[0047] It should also be noted that in-situ X-ray fluorescence probes are installed at appropriate locations within the boiler, such as the furnace outlet ash hopper and tail flue. XRF probes use X-rays to excite elements in the ash sample, causing them to emit characteristic X-ray fluorescence. By detecting the energy and intensity of these characteristic fluorescence signals, the content of various elements in the ash is determined based on XRF analysis principles and corresponding quantitative analysis algorithms. After determining the content of each ash component, simple mathematical calculations, such as dividing the content of a particular element by the sum of the contents of all detected elements, are used to determine the mass percentage of that element in the ash. The proportions of other elements can be calculated similarly, ultimately determining the proportional relationships between the various ash components.

[0048] Once again, it's important to note that a laser particle size analyzer combined with image analysis software is used to contactlessly determine the sphericity of ash particles. The laser particle size analyzer emits a laser beam at the ash particle sample and measures the particle size and distribution based on the laser's scattering characteristics. The instrument's image analysis system processes and analyzes the image formed by the scattered light. Using a specific algorithm, it identifies the outlines of individual ash particles and calculates the ratio of the particle's actual surface area to the surface area of ​​a sphere of the same volume, thereby determining the sphericity of the ash particles. X-ray computed tomography (CT) technology enables contactless measurement of the porosity of ash particles. CT scanning can image the ash sample from multiple angles, acquiring a large number of two-dimensional cross-sectional images. Image reconstruction algorithms generate a three-dimensional model of the ash sample. Image processing and analysis software then identifies and quantifies the pore structure in the 3D model, calculating the ratio of the pore volume to the total sample volume, which represents the ash particle porosity. Ultrasonic measurement allows contactless measurement of the ash bulk density. Ultrasonic transmitters and receivers are installed above the ash accumulation area. When ultrasonic waves propagate through the ash, their propagation velocity is correlated with its density. By measuring the ultrasonic propagation velocity within the ash and combining it with a pre-established velocity-density calibration model, the ash bulk density can be calculated. Alternatively, microwave reflection technology can be used. When microwaves interact with the ash, the characteristics of the reflected signal correlate with the bulk density of the ash. The ash bulk density can be determined by analyzing the reflected microwave signal.

[0049] D2. The flame length and flame skewness corresponding to each boiler at each biomass collection time point are used as input information, normalized, and entered into the combustion property adaptation value analysis model. After calculation and analysis by the combustion property adaptation value analysis model, the combustion property adaptation value corresponding to each boiler at each biomass collection time point is finally output. .

[0050] It should be noted that the analysis process of the combustion property adaptation value corresponding to each boiler at each biomass collection time point is as follows: the flame length and flame deflection corresponding to each boiler at each biomass collection time point are recorded as and , substitute into the analytical formula , and obtain the combustion property adaptation value corresponding to each boiler at each biomass collection time point.

[0051] D3. The content and ratio of each ash component in each boiler at each biomass collection time point are used as input information, normalized, and entered into the chemical property adaptation value analysis model. After calculation and analysis by the chemical property adaptation value analysis model, the chemical property adaptation value corresponding to each boiler at each biomass collection time point is finally output. .

[0052] It should be noted that the chemical property adaptation value corresponding to each boiler at each biomass collection time point is obtained by analyzing the combustion property adaptation value corresponding to each boiler at each biomass collection time point described above.

[0053] D4. The sphericity, porosity and bulk density of the ash particles corresponding to each boiler at each biomass collection time point are normalized and entered into the ash morphology adaptation value analysis model. After calculation and analysis by the ash morphology adaptation value analysis model, the ash morphology adaptation value corresponding to each boiler at each biomass collection time point is finally output. .

[0054] It should be noted that the ash morphology adaptation value corresponding to each boiler at each biomass collection time point is obtained by analyzing the combustion property adaptation value corresponding to each boiler at each biomass collection time point described above.

[0055] Blending control strategy analysis module: used to analyze the blending control strategy corresponding to each boiler at each biomass collection time point based on the slagging risk assessment value corresponding to each boiler at each biomass collection time point.

[0056] In a specific embodiment, the co-firing control strategy corresponding to each boiler at each biomass collection time point is analyzed, and the specific analysis process is as follows: F1. Analyze the slagging risk level corresponding to each boiler at each biomass collection time point, and the slagging risk level includes low risk, medium risk and high risk.

[0057] F2. If the slagging risk level for a boiler at a certain biomass collection time point is low, the conventional biomass and coal blending ratio will be increased by 15%-25% based on the initial blending ratio. At the same time, the special biomass and coal blending ratio will be increased to 10%-12% based on the initial blending ratio.

[0058] It should be noted that conventional biomass refers to biomass with a low slagging risk and relatively stable physical and chemical properties, such as agricultural and forestry waste (rice husks, corn stalks, sawdust, etc.) and municipal waste-derived fuel (RDF). This type of biomass has a low alkali metal content and a high ash melting point, making it less likely to cause serious slagging during combustion. The co-combustion ratio can be moderately increased under low-risk conditions. Special biomass, which often has a high slagging tendency or contains special components, includes palm shells (high in alkali metals), sewage sludge (high in chlorine and moisture), and industrial organic waste (complex composition). Combustion of these biomass is prone to producing low-melting-point ash or releasing corrosive gases. Even under low-risk conditions, the increase in the co-combustion ratio must be strictly controlled to avoid potential slagging and equipment corrosion risks.

[0059] F3. If the slagging risk level of a boiler at a certain biomass collection time point is medium risk, then the conventional biomass and coal blending ratio shall be reduced by 15%-25% based on the initial blending ratio. At the same time, the special biomass and coal blending ratio shall be reduced to 30%-50% of the original ratio based on the initial blending ratio.

[0060] F4. If the slagging risk level for a boiler at a certain biomass collection time point is high, then the blending ratio of conventional biomass and coal shall be reduced to below 10% based on the initial blending ratio. At the same time, all special biomass blending shall be immediately suspended, and high-pressure water flushing shall be started to remove any molten slag that has formed. The soot blower mode shall be switched to continuous blowing, and the whole furnace shall be blown every 30 minutes.

[0061] In a specific embodiment, the slagging risk level corresponding to each boiler at each biomass collection time point is analyzed, and the specific analysis process is as follows: the slagging risk assessment value corresponding to each boiler at each biomass collection time point is compared with the slagging risk assessment value interval corresponding to each slagging risk level in the database. If the slagging risk assessment value corresponding to a boiler at a certain biomass collection time point is within the slagging risk assessment value interval corresponding to a certain slagging risk level in the database, then the slagging risk level in the database is used as the slagging risk level corresponding to the boiler at the biomass collection time point. In this way, the slagging risk level corresponding to each boiler at each biomass collection time point is analyzed.

[0062] It should be noted that the database is used to store the slagging risk assessment value intervals corresponding to each slagging risk level.

[0063] The present invention is implemented as follows Figure 2 As shown, the real-time monitoring information control method based on a thermal power plant includes: Step 1: Acquisition of coal quality data: Install data collection equipment in each boiler of the target thermal power plant to obtain the coal quality data corresponding to each boiler, and then analyze and obtain the coal quality assessment value corresponding to each boiler; Step 2: Boiler combustion feedforward control: Based on the coal quality assessment value corresponding to each boiler, the combustion feedforward control strategy corresponding to each boiler is analyzed.

[0064] Step 3. Obtaining the biomass co-combustion characteristics: After each boiler is adjusted according to the corresponding combustion feedforward control strategy, several biomass collection time points are set to collect the combustion property index, chemical property index and ash morphology index corresponding to each boiler at each biomass collection time point, and then analyze the slagging risk assessment value corresponding to each boiler at each biomass collection time point.

[0065] Step 4: Analysis of the co-firing control strategy: Based on the slagging risk assessment value corresponding to each boiler at each biomass collection time point, the co-firing control strategy corresponding to each boiler at each biomass collection time point is analyzed.

[0066] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present invention.

Claims

1. A real-time monitoring information system based on a thermal power plant, characterized by: include: Incoming coal quality data acquisition module: used to install data collection equipment in each boiler of the target thermal power plant, thereby obtaining the corresponding coal quality data of each boiler, and then analyzing and obtaining the corresponding coal quality assessment value of each boiler; Boiler combustion feedforward control module: used to analyze the combustion feedforward control strategy corresponding to each boiler based on the coal quality assessment value corresponding to each boiler; Biomass blending characteristics acquisition module: After each boiler is adjusted according to the corresponding combustion feedforward control strategy, several biomass collection time points are set to collect the combustion property index, chemical property index and ash morphology index corresponding to each boiler at each biomass collection time point, and then analyze the slagging risk assessment value corresponding to each boiler at each biomass collection time point; Blending control strategy analysis module: used to analyze the blending control strategy corresponding to each boiler at each biomass collection time point based on the slagging risk assessment value corresponding to each boiler at each biomass collection time point.

2. The real-time monitoring information system based on a thermal power plant according to claim 1, characterized in that: The installation of data collection equipment in each boiler of the target thermal power plant is carried out as follows: A1. LIBS module installation: Locate a location 1.5 meters from the coal mill outlet pipe of each boiler and install the LIBS module there. Use an inclined mounting bracket to secure the LIBS module to the bracket and adjust the bracket angle so that the LIBS module is installed at a 30° angle to the coal mill outlet pipe. A2. Terahertz module installation: After installing the LIBS module, use the axis of the LIBS module as a reference and use an axis calibration instrument to ensure that the terahertz module installation position is coaxial with the LIBS module. Place the terahertz module on the platform and fine-tune the platform position and angle so that the central axis of the terahertz module coincides with the central axis of the LIBS module. At the same time, use the angle adjustment device to adjust the laser incident angle of the terahertz module to 5°. Then, use welding and riveting to fix the terahertz module to the outer wall of the coal mill outlet pipe corresponding to each boiler. A3. Installation of microwave resonant cavity: Before installing the microwave resonant cavity, first thoroughly clean the outer wall of the pulverized coal pipeline corresponding to each boiler to remove dust, oil stains, rust impurities on the surface, and use sandpaper to polish the installation part to make its surface flat and smooth. Then surround the annular microwave resonant cavity with an inner diameter of 50mm on the outer wall of the pulverized coal outlet pipeline corresponding to each boiler and fix it with the matching clamp or hoop.

3. The real-time monitoring information system based on a thermal power plant according to claim 2, characterized in that: The analysis yields the coal quality assessment value corresponding to each boiler. The specific acquisition process is as follows: B1. Obtain the content of each element corresponding to each boiler based on the LIBS module installed in each boiler, obtain the content of each mineral corresponding to each boiler based on the terahertz module installed in each boiler, and obtain the moisture content and volatile matter content corresponding to each boiler based on the microwave resonant cavity installed in each boiler; B2. Record the element content, mineral content, moisture content and volatile content of each boiler as 、 、 and ,in, Indicates the number corresponding to each boiler, , u is a positive integer, Indicates the number corresponding to each element, , is a positive integer, It is also a collection of elements. Indicates the number corresponding to each mineral, , is a positive integer, is the collection of all minerals, substitute into the calculation formula: The coal quality evaluation value corresponding to each boiler is obtained ,in, 、 、 and They are the maximum values ​​of the statistical data of each element content, the maximum values ​​of the statistical data of each mineral content, the maximum values ​​of the statistical data of the moisture content and the maximum values ​​of the statistical data of the volatile content in the historical period of the target thermal power plant. 、 、 、 are the weight factors corresponding to the set content of each element in the boiler, the weight factors corresponding to the content of each mineral, the weight factors corresponding to the moisture content, and the weight factors corresponding to the volatile content, and e is expressed as a natural constant.

4. The real-time monitoring information system based on a thermal power plant according to claim 3, characterized in that: The combustion feedforward control strategy corresponding to each boiler is analyzed, and the specific analysis process is as follows: C1. Analyze the coal quality grades corresponding to each boiler. Coal quality grades include high-quality coal, medium-quality coal and low-quality coal; C2. If the coal quality grade corresponding to a boiler is high-quality coal, the primary air volume adjustment is: reduce the air volume by 3.2%. The pulverizer speed control is: reduce the target coal fineness to 12% and increase the speed by 2.4%. The combustion optimization instruction is: adjust the burnout damper opening from 0.05 to 35%, increase the upper secondary air volume of the secondary air ratio by 5%, and reduce the urea solution injection rate of the SNCR system in NOx control by 15%. C3. If the coal quality grade corresponding to a boiler is medium, the primary air volume adjustment is: increase the air volume by 1.5%. The pulverizer speed control is: reduce the target coal fineness to 10% and increase the speed by 1.5%. The combustion optimization instruction is: adjust the burnout damper opening to 30%. Dynamic oxygen control is: increase the oxygen setpoint to 3.3%. The soot blowing strategy is: increase the soot blowing frequency to once every three hours. C4. If the coal quality grade corresponding to a boiler is low-quality coal, the primary air volume adjustment is as follows: increase the air volume by 2.5%. For pulverizer speed control, reduce the target pulverized coal fineness to 8% and increase the speed by 1.2%. For combustion optimization instructions, adjust the burnout damper opening from 0.5% to 55%, increase the secondary air swirl intensity by 20%, increase the ammonia injection rate of the SCR system by 25%, increase the limestone slurry supply by 30%, lower the furnace temperature limit by 50°C, and increase the soot blowing frequency to once every two hours.

5. The real-time monitoring information system based on a thermal power plant according to claim 4, characterized in that: The specific analysis process of analyzing the coal quality grade corresponding to each boiler is as follows: The coal quality assessment value corresponding to each boiler is compared with the coal quality assessment value interval corresponding to each set coal quality grade. If the coal quality assessment value corresponding to a boiler is within the coal quality assessment value interval corresponding to a set coal quality grade, the set coal quality grade will be used as the coal quality grade corresponding to the boiler. In this way, the coal quality grade corresponding to each boiler is analyzed.

6. The real-time monitoring information system based on a thermal power plant according to claim 5, characterized in that: The specific analysis process of analyzing the slagging risk assessment value corresponding to each boiler at each biomass collection time point is as follows: Analyze the combustion property adaptation value, chemical property adaptation value and ash morphology adaptation value corresponding to each boiler at each biomass collection time point, and record them as 、 and ,in, Indicates the number corresponding to each boiler, , u is a positive integer, Indicates the number corresponding to each biomass collection time point, , is a positive integer, It is also the sum of each biomass collection time point, substituted into the calculation formula: The slagging risk assessment value corresponding to each boiler at each biomass collection time point is obtained. ,in, 、 、 They are the standard combustion property adaptation value, standard chemical property adaptation value, and standard ash morphology adaptation value corresponding to the set boiler. 、 、 They are the weight factors corresponding to the set boiler combustion property adaptation value, the weight factors corresponding to the chemical property adaptation value, and the weight factors corresponding to the ash morphology adaptation value.

7. The real-time monitoring information system based on a thermal power plant according to claim 6, characterized in that: The analysis of the combustion property adaptation value, chemical property adaptation value and ash morphology adaptation value corresponding to each boiler at each biomass collection time point is as follows: D1. Obtain the combustion property index, chemical property index, and ash morphology index corresponding to each boiler at each biomass collection time point. The combustion property index includes flame length and flame deflection, the chemical property index includes the content and ratio of each ash component, and the ash morphology index includes ash particle sphericity, ash particle porosity, and ash bulk density. D2. The flame length and flame skewness corresponding to each boiler at each biomass collection time point are used as input information, normalized, and entered into the combustion property adaptation value analysis model. After calculation and analysis by the combustion property adaptation value analysis model, the combustion property adaptation value corresponding to each boiler at each biomass collection time point is finally output. ; D3. The content and ratio of each ash component in each boiler at each biomass collection time point are used as input information, normalized, and entered into the chemical property adaptation value analysis model. After calculation and analysis by the chemical property adaptation value analysis model, the chemical property adaptation value corresponding to each boiler at each biomass collection time point is finally output. ; D4. The sphericity, porosity and bulk density of the ash particles corresponding to each boiler at each biomass collection time point are normalized and entered into the ash morphology adaptation value analysis model. After calculation and analysis by the ash morphology adaptation value analysis model, the ash morphology adaptation value corresponding to each boiler at each biomass collection time point is finally output. .

8. The real-time monitoring information system based on a thermal power plant according to claim 7, characterized in that: The above analysis of the co-firing control strategy corresponding to each boiler at each biomass collection time point is as follows: F1. Analyze the slagging risk level of each boiler at each biomass collection time point. The slagging risk levels include low risk, medium risk, and high risk. F2. If the slagging risk level for a boiler at a given biomass collection time point is low, the conventional biomass and coal blending ratio will be increased by 15%-25% based on the initial blending ratio. At the same time, the special biomass and coal blending ratio will be increased to 10%-12% based on the initial blending ratio. F3. If the slagging risk level for a boiler at a certain biomass collection time point is medium, then the blending ratio of conventional biomass and coal shall be reduced by 15%-25% based on the initial blending ratio. At the same time, the blending ratio of special biomass and coal shall be reduced to 30%-50% based on the initial blending ratio. F4. If the slagging risk level for a boiler at a certain biomass collection time point is high, then the blending ratio of conventional biomass and coal shall be reduced to below 10% based on the initial blending ratio. At the same time, all special biomass blending shall be immediately suspended, and high-pressure water flushing shall be started to remove any molten slag that has formed. The soot blower mode shall be switched to continuous blowing, and the whole furnace shall be blown every 30 minutes.

9. The real-time monitoring information system based on a thermal power plant according to claim 8, characterized in that: The specific analysis process for analyzing the slagging risk level of each boiler at each biomass collection time point is as follows: The slagging risk assessment value corresponding to each boiler at each biomass collection time point is compared with the slagging risk assessment value interval corresponding to each slagging risk level in the database. If the slagging risk assessment value corresponding to a boiler at a certain biomass collection time point is within the slagging risk assessment value interval corresponding to a certain slagging risk level in the database, the slagging risk level in the database will be used as the slagging risk level corresponding to the boiler at the biomass collection time point. In this way, the slagging risk level corresponding to each boiler at each biomass collection time point is analyzed.

10. A method for controlling real-time monitoring information based on a thermal power plant, which implements the real-time monitoring information system based on a thermal power plant according to any one of claims 1 to 9, characterized in that: include: Step 1: Acquisition of coal quality data: Install data collection equipment in each boiler of the target thermal power plant to obtain the coal quality data corresponding to each boiler, and then analyze and obtain the coal quality assessment value corresponding to each boiler; Step 2: Boiler combustion feedforward control: Based on the coal quality assessment value of each boiler, the combustion feedforward control strategy corresponding to each boiler is analyzed; Step 3: Obtaining biomass blending characteristics: After each boiler is adjusted according to the corresponding combustion feedforward control strategy, several biomass collection time points are set to collect the combustion property index, chemical property index, and ash morphology index corresponding to each boiler at each biomass collection time point, and then analyze the slagging risk assessment value corresponding to each boiler at each biomass collection time point; Step 4: Analysis of the co-firing control strategy: Based on the slagging risk assessment value corresponding to each boiler at each biomass collection time point, the co-firing control strategy corresponding to each boiler at each biomass collection time point is analyzed.

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