A multi-factor synergistic coal powder regulation system and method based on an external powder bin of a boiler

Abstract

The application discloses a multi-factor coordinated coal powder regulation system and method based on an external powder bin of a boiler, and relates to the technical field of thermal power generation. The system comprises a data acquisition module, an intelligent regulation module, a powder taking device and a powder feeding fan; the intelligent regulation module is connected with the data acquisition module, the powder taking device and the powder feeding fan; the data acquisition module is used for acquiring coal powder characteristic parameters, pipeline pressure parameters and conveying distance parameters; the intelligent regulation module is used for calculating the powder taking amount and the powder feeding speed through a fuzzy control algorithm or a neural network algorithm according to the coal powder characteristic parameters, the pipeline pressure parameters and the conveying distance parameters, controlling the powder taking device to take coal powder from the external powder bin of the boiler according to the powder taking amount, and controlling the powder feeding fan to convey the coal powder to the boiler according to the powder feeding speed. The system realizes accurate control over the powder taking and powder feeding processes.

Description

Technical Field

[0001] This invention relates to the field of thermal power generation technology, and in particular to a multi-factor collaborative pulverized coal control system and method based on an external pulverized coal silo in a boiler. Background Technology

[0002] In thermal power generation systems, boilers are the core equipment that converts the chemical energy of fuel into thermal energy, and pulverized coal, as the main fuel, requires a stable and efficient supply for normal boiler operation. To meet the continuous demand for pulverized coal from boilers, and to cope with fluctuations in coal supply and peak-shaving requirements of the unit, many thermal power units are equipped with external pulverized coal silos to store a certain amount of pulverized coal.

[0003] Most existing coal pulverizer collection and delivery control methods adopt fixed parameter control strategies. In the process of collecting coal pulverizer from the external coal silo and transporting it to the boiler, problems such as poor coal pulverizer transport, blockage, and coal pulverizer accumulation often occur. This not only affects the boiler's combustion efficiency and stability but also increases equipment maintenance costs and safety risks. Summary of the Invention

[0004] Therefore, it is necessary to provide a multi-factor collaborative pulverized coal control system and method based on an external pulverized coal bin for boilers to address the above-mentioned technical problems. This system achieves precise control over the pulverized coal extraction and delivery process, which not only improves the boiler's combustion efficiency and stability but also reduces equipment maintenance costs and safety risks.

[0005] The present invention adopts the following technical solution: This invention provides a multi-factor coordinated pulverized coal control system based on an external pulverized coal silo in a boiler, comprising: The system includes: a data acquisition module, an intelligent control module, a powder collection device, and a powder delivery fan; the intelligent control module is connected to the data acquisition module, the powder collection device, and the powder delivery fan respectively. The data acquisition module is used to collect coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters. The coal powder characteristic parameters include coal powder particle size distribution parameters, moisture parameters, and flowability parameters. The pipeline pressure parameters include coal powder intake pipeline pressure parameters and coal powder delivery pipeline pressure parameters. The conveying distance parameter is the length of the coal powder conveying path from the external coal silo to the boiler. The intelligent control module is used to calculate the amount of coal powder taken and the coal powder delivery speed based on the coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters through fuzzy control algorithms or neural network algorithms. Based on the amount of coal powder taken, it controls the coal powder taking equipment to take coal powder from the external coal silo of the boiler, and controls the coal powder delivery fan to deliver the coal powder to the boiler based on the coal powder delivery speed.

[0006] Optionally, the data acquisition module includes a sensor group for detecting coal powder characteristic parameters, a pressure sensor for detecting pipeline pressure parameters, and a positioning module for acquiring conveying distance parameters; the pressure sensors are respectively installed at the inlet section, intermediate section, and outlet section of the coal powder intake pipeline, as well as at the starting end, intermediate pressurization point, and boiler inlet of the coal powder delivery pipeline; the sensor group includes a particle size analyzer, a humidity sensor, and a rheometer for detecting coal powder flowability.

[0007] Optionally, the intelligent control module includes a data processing unit and an algorithm calculation unit; The data processing unit is used to filter, reduce noise, and normalize the collected coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters. The algorithm calculation unit incorporates fuzzy control and neural network algorithms to calculate the powder intake and powder delivery speed based on the processed data.

[0008] Optionally, the powder collection device is a spiral powder collector equipped with a variable frequency motor. The variable frequency motor adjusts its speed according to the instructions of the intelligent control module to control the amount of powder collected. The powder collection amount adjustment range is 0~100%, and the adjustment accuracy is ±1%.

[0009] Optionally, the powder conveying fan is a centrifugal fan equipped with a variable frequency speed control device. The variable frequency speed control device adjusts the fan speed according to the instructions of the intelligent control module, so that the air volume of the powder conveying fan can be adjusted within the range of 1000~10000 m³ / h, and the response time is less than 1 s.

[0010] This invention provides a multi-factor coordinated pulverized coal control method based on an external pulverized coal silo in a boiler, the method comprising: Collect coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters; coal powder characteristic parameters include coal powder particle size distribution parameters, moisture parameters, and flowability parameters; pipeline pressure parameters include coal powder intake pipeline pressure parameters and coal powder delivery pipeline pressure parameters; conveying distance parameters are the coal powder conveying path length from the external coal powder silo to the boiler; Based on the pulverized coal characteristic parameters, pipeline pressure parameters, and conveying distance parameters, the pulverized coal extraction amount and conveying speed are calculated using fuzzy control algorithms or neural network algorithms. The extraction amount is used to control the pulverized coal extraction equipment to extract pulverized coal from the external pulverized coal silo of the boiler, and the conveying speed is used to control the conveying fan to transport the pulverized coal to the boiler.

[0011] Optionally, the powder dispensing amount and powder delivery speed are calculated using a fuzzy control algorithm, including: The pulverized coal characteristic parameters, pipeline pressure parameters, and conveying distance parameters are converted into fuzzy linguistic variables. Through preset fuzzy inference rules, the adjustment amounts of the operating parameters of the pulverized coal extraction equipment and the pulverized coal conveying fan are output.

[0012] Optionally, the powder dispensing amount and powder delivery speed are calculated using a neural network algorithm, including: By using a neural network algorithm to model the nonlinear mapping relationship between the amount of coal powder taken and the coal powder delivery speed and the coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters, the coal powder taking amount and delivery speed corresponding to the current coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters are determined.

[0013] This invention provides a multi-factor coordinated pulverized coal control device based on an external pulverized coal silo in a boiler, comprising: The data acquisition module is used to collect coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters. The coal powder characteristic parameters include coal powder particle size distribution parameters, moisture parameters, and flowability parameters. The pipeline pressure parameters include coal powder intake pipeline pressure parameters and coal powder delivery pipeline pressure parameters. The conveying distance parameter is the length of the coal powder conveying path from the external coal silo to the boiler. The control module is used to calculate the amount of coal powder taken and the coal powder delivery speed based on the coal powder characteristic parameters, pipeline pressure parameters and conveying distance parameters, using fuzzy control algorithm or neural network algorithm. Based on the amount of coal powder taken, it controls the coal powder taking equipment to take coal powder from the external coal silo of the boiler, and based on the coal powder delivery speed, it controls the coal powder delivery fan to deliver the coal powder to the boiler.

[0014] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multi-factor coordinated pulverized coal control method based on an external pulverized coal silo in a boiler.

[0015] The present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned multi-factor coordinated pulverized coal control method based on an external pulverized coal silo in a boiler.

[0016] The above-mentioned at least one technical solution adopted in this invention can achieve the following beneficial effects: This invention breaks through the limitations of traditional single-factor control by comprehensively considering the influence of multiple factors such as pulverized coal characteristics, pipeline pressure, and conveying distance. In terms of pulverized coal characteristics, it conducts in-depth analysis of key parameters such as pulverized coal particle size distribution, humidity, and flowability. For pipeline pressure, it monitors the pressure change trend in the pulverized coal extraction and delivery pipelines in real time. At the same time, for the pulverized coal conveying pipeline with a fuzzy control or neural network artificial intelligence algorithm, it introduces fuzzy control or neural network artificial intelligence algorithms to achieve precise control of the pulverized coal extraction and delivery process. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0018] Figure 1This invention provides a schematic diagram of a multi-factor collaborative pulverized coal control system based on an external pulverized coal silo in a boiler. Figure 2 This invention provides a schematic flowchart of a multi-factor collaborative pulverized coal control method based on an external pulverized coal silo in a boiler. Figure 3 This invention provides a schematic diagram of a computer device for implementing a multi-factor coordinated pulverized coal control method based on an external pulverized coal silo in a boiler. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0020] Currently, there are numerous problems in the process of extracting pulverized coal from the external pulverized coal silo and transporting it to the boiler. In the modern thermal power unit operation system, the external pulverized coal silo serves as a key hub for pulverized coal storage and supply, and its efficient and stable operation in the extraction and delivery stages plays a decisive role in the overall performance of the unit. Traditional pulverized coal transportation methods are affected by factors such as fluctuations in pulverized coal characteristics, changes in pipeline pressure, and limitations in transportation distance, leading to frequent blockages and pulverized coal accumulation during transportation. The equipment response speed is slow, making it difficult to adapt to rapid changes in boiler load. This not only reduces pulverized coal transportation efficiency but also seriously threatens the safety and reliability of system operation, becoming a bottleneck restricting the efficient and stable operation of thermal power units. On the one hand, pulverized coal characteristics such as particle size distribution, moisture content, and flowability vary depending on factors such as coal source and storage conditions, and these differences significantly affect the transportation performance of pulverized coal. For example, pulverized coal with high moisture content tends to clump together, causing pipeline blockage; larger particle sizes may deposit inside the pipeline, affecting the uniformity of transportation. On the other hand, pipeline pressure and transportation distance are also key factors affecting pulverized coal transportation. Unstable pipeline pressure may lead to uneven coal powder conveying speed or even backflow; while a longer conveying distance will increase the friction between coal powder and the pipeline wall, reduce conveying efficiency, and may also cause coal powder to accumulate at the bottom of the pipeline due to gravity.

[0021] Existing pulverized coal feeding and control methods mostly employ fixed-parameter control strategies, making it difficult to dynamically adjust based on real-time changes in pulverized coal characteristics, pipeline pressure, and conveying distance. This frequently leads to problems such as poor pulverized coal conveying, blockages, and pulverized coal accumulation during actual operation, affecting boiler combustion efficiency and stability, and increasing equipment maintenance costs and safety risks. Furthermore, when thermal power units require load adjustments, existing control methods cannot respond quickly and accurately, resulting in a mismatch between pulverized coal supply and boiler load demand, further reducing the unit's operating efficiency and flexibility.

[0022] Therefore, to address the aforementioned challenges, there is an urgent need for a system and method that can comprehensively consider multiple factors such as pulverized coal characteristics, pipeline pressure, and conveying distance to achieve intelligent control of pulverized coal extraction and delivery. This would improve the stability and efficiency of pulverized coal delivery and ensure the safe and efficient operation of thermal power units. This method comprehensively considers multiple dimensions of factors, including pulverized coal characteristics, pipeline pressure, and conveying distance. Through advanced control algorithms and high-precision equipment, it achieves precise and dynamic control of the pulverized coal extraction equipment and the pulverized coal delivery fan. This ensures that pulverized coal is delivered to the boiler in a uniform and stable manner, thereby improving the safety, reliability, and efficiency of thermal power unit operation, reducing maintenance costs and energy consumption caused by equipment failures, and meeting the urgent needs of the power industry for efficient and stable operation and intelligent upgrading of thermal power units.

[0023] Specifically, this invention discloses a multi-factor collaborative pulverized coal control system based on an external pulverized coal silo in a boiler. The system's core objective is to construct an intelligent and precise pulverized coal conveying control system. By comprehensively considering multiple influencing factors such as pulverized coal characteristics, pipeline pressure, and conveying distance, it breaks through the limitations of traditional single-factor control. Regarding pulverized coal characteristics, it deeply analyzes key parameters such as particle size distribution, humidity, and flowability, establishing a database of conveying characteristics for pulverized coal with different properties. For pipeline pressure, it monitors the pressure change trends within the pulverized coal intake and delivery pipelines in real time, dynamically adjusting the conveying strategy based on the impact of pressure fluctuations on pulverized coal conveying. Simultaneously, it optimizes the air pressure and airflow configuration of the pulverized coal delivery fan for conveying pipelines of different lengths, ensuring that the pulverized coal maintains a stable flow rate and uniform distribution during long-distance conveying. The introduction of artificial intelligence algorithms such as fuzzy control and neural networks enables precise control of the pulverized coal intake and delivery processes. This system is not only suitable for conventional thermal power units, but also ensures the stable operation of the pulverized coal conveying system under complex operating conditions where new energy and thermal power are operated in tandem. It has important application value and broad prospects for promotion in the field of power energy, and provides strong technical support for promoting the development of the thermal power industry towards high efficiency, intelligence and greenness.

[0024] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0025] First, the multi-factor collaborative pulverized coal control system based on an external pulverized coal silo provided by this invention will be described, such as... Figure 1 As shown, Figure 1 This is a schematic diagram of a multi-factor collaborative pulverized coal control system based on an external pulverized coal bin in the present invention. The system includes: a data acquisition module, an intelligent control module, a pulverized coal extraction device, and a pulverized coal delivery fan; the intelligent control module is connected to the data acquisition module, the pulverized coal extraction device, and the pulverized coal delivery fan.

[0026] The data acquisition module is used to collect coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters. The coal powder characteristic parameters include coal powder particle size distribution parameters, humidity parameters, and flowability parameters. The pipeline pressure parameters include coal powder intake pipeline pressure parameters and coal powder delivery pipeline pressure parameters. The conveying distance parameter is the length of the coal powder conveying path from the external coal silo of the boiler to the boiler.

[0027] The intelligent control module is used to calculate the amount of coal powder taken and the coal powder delivery speed based on the coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters through fuzzy control algorithms or neural network algorithms. Based on the amount of coal powder taken, it controls the coal powder taking equipment to take coal powder from the external coal silo of the boiler, and controls the coal powder delivery fan to deliver the coal powder to the boiler based on the coal powder delivery speed.

[0028] The system's modules communicate with each other via a combination of industrial Ethernet and fieldbus to exchange data and transmit commands. The data acquisition module connects to various sensors via RS485 bus, transmitting collected coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters to the intelligent control module. The intelligent control module, built on a programmable logic controller (PLC), connects to the control units of the coal powder extraction equipment and the coal powder delivery fan via Profinet industrial Ethernet to send control commands. The system is equipped with a human-machine interface, allowing operators to view system operating data, set parameters, and monitor equipment status in real time, achieving visualized management of the entire coal powder extraction and delivery process.

[0029] Optionally, the data acquisition module includes a sensor group for detecting coal powder characteristic parameters, a pressure sensor for detecting pipeline pressure parameters, and a positioning module for acquiring conveying distance parameters; the pressure sensors are respectively installed at the inlet section, intermediate section, and outlet section of the coal powder intake pipeline, as well as at the starting end, intermediate pressurization point, and boiler inlet of the coal powder delivery pipeline; the sensor group includes a particle size analyzer, a humidity sensor, and a rheometer for detecting coal powder flowability.

[0030] The coal powder characteristic parameter acquisition includes: a particle size analyzer that calculates particle size distribution using Mie scattering theory, covering a detection range of 0.01~3500 μm, capable of completing a full-range scan within 1 minute, and outputting characteristic particle size data such as D10, D50, and D90. A humidity sensor based on a capacitive humidity sensing element achieves a measurement accuracy of ±1% RH within a humidity range of 0~100% RH, with a response time of less than 5 seconds. A rheometer employs a cone-plate measurement system, capable of measuring from 0.01 to 1000 s. -1 Within the range of shear rates, the apparent viscosity, storage modulus, loss modulus, and other rheological parameters of pulverized coal are accurately measured, providing comprehensive data support for the analysis of pulverized coal flowability.

[0031] Pipeline pressure parameter acquisition: Pressure sensors are installed at the inlet, middle and outlet sections of the pulverized coal intake pipeline, as well as at the starting end, intermediate pressurization point and boiler inlet of the pulverized coal delivery pipeline. The range can be selected from 0 to 1.6 MPa, with an accuracy of ±0.1% FS. They are equipped with HART communication protocol and can transmit pressure data to the intelligent control module in real time in the form of digital signals. The sampling frequency is 10 times / second, which can capture small fluctuations in pipeline pressure.

[0032] Conveying distance parameter acquisition: The positioning module combines a receiver with high-precision map data, using real-time dynamic positioning technology to achieve centimeter-level positioning of the pulverized coal conveying pipeline. The system pre-loads the three-dimensional coordinate information of the pipeline layout and calculates the spatial distance between the pipeline's starting and ending points to accurately obtain the pulverized coal conveying path length from the external pulverized coal silo to the boiler, with an error controlled within ±0.1 m. Simultaneously, the positioning module can also monitor pipeline position changes caused by factors such as thermal expansion and contraction, providing a reference for adjusting conveying parameters.

[0033] Optionally, the intelligent control module includes a data processing unit and an algorithm calculation unit; the data processing unit is used to filter, reduce noise, and normalize the collected coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters; the algorithm calculation unit has built-in fuzzy control algorithm and neural network algorithm, which are used to calculate the coal powder intake and delivery speed based on the processed data.

[0034] Data Processing Unit: During data preprocessing, the median filtering algorithm uses a 3×3 sliding window to process data collected from each sensor, effectively removing interference noise. The Kalman filtering algorithm is based on a state-space model; taking pressure sensor data processing as an example, its state equation is:

[0035] Its measurement equation is: in, X kLet be the system state vector. A Here is the state transition matrix. B To control the input matrix, u k To control the input vector, w k For process noise, Z k For measurement vectors, H For the measurement matrix, v k For measuring noise.

[0036] By continuously updating the state estimate and covariance matrix, real-time filtering of pressure data is achieved, improving data accuracy. Normalization is performed using the minimum-maximum normalization method, with the following formula:

[0037] The above formula can be used to uniformly map data of different ranges to the interval [0, 1].

[0038] Algorithm Calculation Unit: Fuzzy inference processes the input fuzzified parameters (normalized pulverized coal characteristic parameters, pipeline pressure parameters, and conveying distance parameters) according to established fuzzy rules to obtain the output fuzzy quantities. Fuzzy inference is performed based on the normalized pulverized coal characteristic parameters, pipeline pressure parameters, and conveying distance parameters to obtain the pulverized coal extraction rate and conveying speed; alternatively, the normalized pulverized coal characteristic parameters, pipeline pressure parameters, and conveying distance parameters are input into a neural network algorithm to obtain the pulverized coal extraction rate and conveying speed.

[0039] Optionally, the powder collection device is a spiral powder collector equipped with a variable frequency motor. The variable frequency motor adjusts its speed according to the instructions of the intelligent control module to control the amount of powder collected. The powder collection amount adjustment range is 0~100%, and the adjustment accuracy is ±1%.

[0040] The spiral dust collector features a variable pitch design for its spiral blades. The pitch is smaller near the dust bin outlet to prevent coal dust blockage, while the pitch is larger further away to improve dust collection efficiency. A Siemens G120C series variable frequency motor with a power of 15kW and a speed range of 0~1500 r / min is used, with a speed control accuracy of ±1 r / min. The motor and spiral shaft are connected via a flexible coupling to ensure stable power transmission. The intelligent control module sends speed control commands to the frequency converter via the Modbus TCP protocol. The frequency converter adjusts the motor frequency according to the commands, thereby controlling the dust collection rate of the spiral dust collector. The relationship between dust collection rate and motor speed was calibrated experimentally, and a mathematical model was established as follows: Q =0.01 n ,in Q This refers to the amount of powder taken (t / h). nThe motor speed (r / min) can be adjusted with an accuracy of ±1%.

[0041] Optionally, the powder conveying fan is a centrifugal fan equipped with a variable frequency speed control device. The variable frequency speed control device adjusts the fan speed according to the instructions of the intelligent control module, so that the air volume of the powder conveying fan can be adjusted within the range of 1000~10000 m³ / h, and the response time is less than 1 s.

[0042] The centrifugal fan features a backward-curved blade design on its impeller, improving efficiency and stability. The variable frequency drive (VFD) uses the ABB ACS880 series, with an input voltage of 380 V and an output frequency range of 0-50 Hz, enabling continuous adjustment of the fan speed within the range of 500-3000 r / min. The fan's airflow and speed follow a similarity law, approximately satisfying Q1 / Q2=n1 / n2 within a certain range, where Q1 and Q2 are the airflow at different speeds, and n1 and n2 are the corresponding speeds. For example, if the airflow at speed n1 is Q1, and the required airflow is Q2, then based on the proportional relationship between airflow and speed, the speed can be calculated. The intelligent control module sends speed control signals to the variable frequency speed control device via the Profinet protocol based on the calculated coal feeding speed. The variable frequency speed control device has a response time of less than 1 second and can quickly adjust the fan speed, so that the air volume of the coal feeding fan can be precisely varied within the range of 1000~10000 m3 / h. At the same time, by adjusting the angle of the fan inlet guide vanes, the air pressure is optimized to ensure that the pulverized coal is delivered to the boiler at a suitable speed and pressure.

[0043] In one embodiment, the present invention also provides system installation, system debugging, system operation and management, and equipment maintenance.

[0044] 1. System installation specifically includes: (1) Equipment layout planning In one embodiment, at the thermal power unit site, the installation locations of each device in the intelligent coal pulverizing and conveying control system are rationally planned based on the location of the external pulverized coal silo, the boiler, and other related equipment. Various sensors from the data acquisition module are installed at key nodes in the pulverized coal conveying pipeline and the silo. For example, a particle size analyzer is installed at the silo outlet to detect the particle size of the coal pulverized coal to be conveyed in real time; pressure sensors are installed at the inlet, intermediate section, and outlet of the coal pulverizing and conveying pipelines according to design requirements; humidity sensors and rheometers are installed inside the silo to ensure accurate acquisition of coal pulverized coal characteristic data. The coal pulverizing equipment is installed at the bottom of the silo, and the conveying fan is installed near the silo in a location convenient for pipeline connection. The PLC control cabinet and human-machine interface of the intelligent control module are installed in the unit control room for convenient monitoring and management by operators.

[0045] (2) Equipment installation and connection Sensor Installation: Strictly follow the sensor installation instructions to ensure secure installation and that it does not affect the normal operation of pipelines and coal silos. For pressure sensors, clean and seal the installation interface before installation to prevent air leakage from affecting measurement accuracy. When installing particle size analyzers, humidity sensors, and rheometers, ensure that their measuring probes are in full contact with the coal powder and do not obstruct its flow. After installation, perform preliminary testing on the sensors to check if they can function properly and output data.

[0046] Installation of Powder Collector and Powder Conveying Fan: When installing the spiral powder collector, ensure the concentricity of the spiral shaft and the powder bin outlet to avoid uneven powder collection or equipment vibration due to eccentricity. Adjust the gap between the spiral blades and the inner wall of the powder bin to 5-10 mm to prevent coal powder accumulation. The variable frequency motor is installed on a stable foundation near the powder collector and reliably connected to the spiral shaft via a flexible coupling. When installing the centrifugal fan, ensure the fan base is level and firmly fixed to the foundation to prevent vibration during operation. Use flexible connections between the fan's inlet and outlet and the pipeline to reduce vibration transmission and noise. The variable frequency speed control device is installed in the control cabinet near the fan; connect it correctly according to the electrical wiring diagram.

[0047] Communication and control line connections: The sensors of the data acquisition module are connected to the PLC input module of the intelligent control module via an RS485 bus. Ensure correct wiring and proper shielding of the communication lines to prevent signal interference. The intelligent control module is connected to the control units of the powder collection equipment and the powder conveying fan via Profinet industrial Ethernet, using dedicated industrial Ethernet cables and wiring according to the network topology. The human-machine interface is connected to the PLC of the intelligent control module via a network cable to achieve data interaction and operation control. After all lines are connected, a line check is performed to ensure there are no short circuits or open circuits.

[0048] 2. System debugging specifically includes: (1) Sensor calibration The various sensors in the data acquisition module were calibrated using standard calibration equipment. For pressure sensors, a high-precision pressure calibrator was used to calibrate at pressure points of 0 MPa, 0.5 MPa, 1.0 MPa, and 1.6 MPa, adjusting the sensor's zero point and range to ensure a measurement error within ±0.1% FS. The particle size analyzer was calibrated using standard particle samples to ensure accurate and reliable particle size distribution data. The humidity sensor was calibrated using the saturated salt solution method, measuring under different humidity environments (e.g., 30% RH, 60% RH, 90% RH), adjusting the sensor's output value to achieve an accuracy of ±1% RH. The rheometer was calibrated using standard fluids to ensure accurate measurement of rheological parameters.

[0049] (2) Equipment debugging Powder extraction equipment commissioning: Power on the variable frequency motor of the spiral powder extractor, set different speeds through the human-machine interface, observe the operation of the spiral powder extractor, and check whether the spiral shaft rotates smoothly and whether there is any abnormal noise or vibration. Simultaneously, measure the powder extraction volume at different speeds to verify the mathematical model of powder extraction volume versus motor speed. Q The accuracy is 0.01n. If there is a deviation, the model should be corrected or the equipment parameters adjusted.

[0050] Powder conveying fan commissioning: Start the centrifugal fan and gradually increase the fan speed using the frequency converter. Observe the fan's operating status, check whether the impeller rotation is balanced, whether the bearing temperature is normal, and whether there is any abnormal vibration or noise. Measure the fan's air volume and air pressure at different speeds to verify the relationship between air volume and speed and whether the fan performance meets the design requirements. Adjust the fan inlet guide vane angle and observe the air pressure changes to ensure that effective air pressure control can be achieved by adjusting the guide vane angle.

[0051] System linkage debugging: After sensor calibration and individual equipment debugging are completed, system linkage debugging is performed. Simulating different pulverized coal characteristics, pipeline pressure, and conveying distance data, the intelligent control module calculates the pulverized coal extraction rate and delivery speed, and sends control commands to the extraction equipment and conveying fan. The equipment response and pulverized coal conveying effect are observed. It is checked whether the system can accurately and promptly adjust the extraction rate and delivery speed according to changes in input data, ensuring uniform and stable pulverized coal delivery to the boiler. Simultaneously, the system's communication stability is tested, checking whether data acquisition, transmission, and control command execution are normal, and whether there is any data loss or delay.

[0052] (3) Algorithm optimization During system debugging, actual operating data was collected to optimize the fuzzy control algorithm and neural network algorithm in the intelligent control module. For the fuzzy control algorithm, the fuzzy inference rules and membership function parameters were adjusted based on the actual control effect to make the control more precise. For the neural network algorithm, the data collected during debugging was used as training samples to further train the neural network, optimize the network weights and thresholds, and improve the model's prediction accuracy and control performance. Through continuous adjustment and optimization of the algorithms, the system can achieve the best powder collection and delivery control effect under different operating conditions.

[0053] 3. System Operation and Management (1) Daily operation monitoring Operators can monitor the system's operating status in real time through a human-machine interface, viewing operational data such as pulverized coal characteristic parameters, pipeline pressure parameters, conveying distance parameters, and pulverized coal collection and delivery speed. Alarm thresholds for key parameters can be set; for example, when the pipeline pressure exceeds 1.2 MPa or falls below 0.2 MPa, the system will issue an audible and visual alarm signal to alert the operator. Simultaneously, the system monitors the operating status of equipment, such as motor current, voltage, and temperature, and fan bearing temperature and vibration parameters. If any abnormalities are detected, timely measures will be taken to address them.

[0054] (2) Parameter adjustment and optimization Based on changes in the operating conditions of thermal power units and coal characteristics, operators can manually adjust the parameters of the intelligent control module through the human-machine interface, such as the inference rules of the fuzzy control algorithm and the learning rate of the neural network algorithm. The system can also automatically optimize parameters based on real-time data. For example, when changes in coal origin lead to alterations in pulverized coal characteristics, the neural network algorithm automatically learns the new data and adjusts the control strategies for pulverized coal extraction and delivery speed. Regular analysis of system operating data is conducted to summarize the optimal operating parameters under different conditions, further optimizing system performance.

[0055] (3) Emergency Response Develop comprehensive emergency response plans so that operators can quickly take measures to handle system malfunctions such as sensor failures, equipment shutdowns, and communication interruptions. For sensor failures, promptly replace the sensor with a spare and calibrate the new one. If the powder collection equipment or powder conveying fan shuts down, investigate the cause of the equipment failure, such as motor overload or mechanical failure, and restart the equipment after troubleshooting. When communication is interrupted, inspect the communication lines and network equipment, repair the faulty points, and ensure the system returns to normal operation.

[0056] 4. Equipment maintenance (1) Regular maintenance Develop a regular equipment maintenance plan, and conduct regular inspections and maintenance on powder collection equipment, powder conveying fans, sensors, etc. For spiral powder collectors, check the wear of the spiral blades every month, and replace them promptly if the wear is severe; lubricate and maintain the variable frequency motor every quarter, and check the operating condition of the motor bearings. Clean the dust inside the centrifugal fan every two months, and check the wear of the impeller and bearings; perform electrical performance testing and cleaning maintenance on the variable frequency speed control device every six months. Perform performance testing and calibration on all types of sensors every three months to ensure accurate and reliable measurement data.

[0057] (2) Preventive maintenance Preventative maintenance is performed based on equipment operating conditions and historical data. For example, by analyzing data such as motor operating current and temperature, potential motor failures can be predicted, allowing for early repair or replacement of parts. Regular inspections of pulverized coal conveying pipelines are conducted, especially at bends and diameter changes, to prevent leaks caused by wear. The PLC and software system of the intelligent control module are regularly backed up and upgraded to ensure system stability and reliability.

[0058] This invention also provides a multi-factor coordinated pulverized coal control method based on an external pulverized coal silo in a boiler, such as... Figure 2 As shown, Figure 2 This is a flowchart illustrating the multi-factor coordinated pulverized coal control method based on an external boiler pulverized coal silo provided by the present invention. This method is applied to the aforementioned multi-factor coordinated pulverized coal control system based on an external boiler pulverized coal silo, and specifically includes the following steps: S101 collects coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters; coal powder characteristic parameters include coal powder particle size distribution parameters, humidity parameters, and flowability parameters; pipeline pressure parameters include coal powder intake pipeline pressure parameters and coal powder delivery pipeline pressure parameters; conveying distance parameters are the coal powder conveying path length from the external coal powder silo of the boiler to the boiler.

[0059] The specific limitations on the collection of pulverized coal characteristic parameters, pipeline pressure parameters, and conveying distance parameters can be found in the system limitations section above, and will not be repeated here.

[0060] S102 calculates the amount of coal powder taken and the coal powder delivery speed based on the coal powder characteristic parameters, pipeline pressure parameters and conveying distance parameters through fuzzy control algorithm or neural network algorithm. It controls the coal powder taking equipment to take coal powder from the external coal powder silo of the boiler according to the amount of coal powder taken, and controls the coal powder delivery fan to deliver the coal powder to the boiler according to the coal powder delivery speed.

[0061] In one embodiment, calculating the amount of coal powder taken and the coal powder delivery speed using a fuzzy control algorithm includes: converting coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters into fuzzy linguistic variables, and outputting the amount of coal powder taken by the coal powder taking device and the coal powder delivery speed of the coal powder delivery fan through preset fuzzy inference rules.

[0062] (1) Fuzzy rules: Fuzzy rules are established based on experience and understanding of the system. They usually take the form of "if-then", such as "if the coal powder has high humidity and the pipeline pressure is low, then reduce the amount of coal powder taken and increase the coal powder delivery speed".

[0063] (2) Determine the fuzzy sets of input and output For input parameters, such as coal powder humidity, the parameters are divided into three fuzzy sets: "low", "medium", and "high"; pipeline pressure is divided into three fuzzy sets: "low", "medium", and "high"; and conveying distance is divided into three fuzzy sets: "short", "medium", and "long". For output parameters, the parameters are divided into three fuzzy sets: "low", "medium", and "high"; and coal feeding speed is divided into three fuzzy sets: "slow", "medium", and "fast".

[0064] (3) The coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters are converted into fuzzy linguistic variables. Through preset fuzzy inference rules, the coal powder collection amount of the coal powder collection equipment and the coal powder delivery speed of the coal powder delivery fan are output, specifically including: First, based on the actual values ​​of the input parameters (pulverized coal characteristic parameters, pipeline pressure parameters, and conveying distance parameters), the respective membership functions are used to... ,in, x These are the actual parameter values. c Let σ be the center value of the fuzzy set, and σ be the standard deviation. Calculate their membership degrees (fuzzy linguistic variables) to the corresponding fuzzy set. Then, inference is performed based on fuzzy rules. Taking the rule "If the coal powder moisture is low, the pipeline pressure is low, and the conveying distance is short, then the coal powder intake is high and the coal powder delivery speed is fast" as an example, the activation strength of this rule is... The activation level of a rule is determined by taking the minimum membership degree among the three input parameters. For each rule, the activation strength is calculated using the above method, and its contribution to the output fuzzy set is determined based on the activation strength.

[0065] The output obtained through fuzzy inference is a fuzzy quantity, which needs to be converted into a specific numerical value through defuzzification to actually control the actuator. Defuzzification is performed using the centroid method, calculating the centroid coordinates of the output fuzzy set as the defuzzified result. For example, for the fuzzy set of powder quantity, its centroid coordinates... ,in, It is the first fuzzy concentration of powder amount Membership degree of each element, This is the actual value corresponding to the element (referring to the control value during system operation (such as the amount of powder taken)). The C obtained through calculation is the specific value of the amount of powder taken.

[0066] Through the above steps, based on the fuzzy parameters of pulverized coal humidity, pipeline pressure, and conveying distance, and through fuzzy inference and defuzzification processing, specific control values ​​for the amount of pulverized coal taken and the conveying speed can be obtained, thereby realizing fuzzy control of the pulverized coal conveying system.

[0067] It should be noted that fuzzy rules can be set according to actual needs.

[0068] In one embodiment, calculating the amount of coal powder taken out and the speed of coal powder delivery using a neural network algorithm includes: determining the amount of coal powder taken out and the speed of coal powder delivery corresponding to the current coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters by using a nonlinear mapping relationship model between the amount of coal powder taken out and the speed of coal powder delivery and the coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters in the neural network algorithm.

[0069] The neural network algorithm employs a three-layer structure. The input layer contains 10 neurons, corresponding to three monitoring points related to the coal powder particle size distribution (particle size, humidity, flowability parameter, and pipeline pressure) and the conveying distance. The hidden layer contains 20 neurons, with the ReLU activation function f(x) = max(0,x). The output layer contains two neurons, outputting the control values ​​for the coal powder intake and delivery speed. The training process utilizes the backpropagation algorithm with mean squared error (MSE) as the loss function. The Adam optimizer is used to adjust the network weights, and the learning rate is set to 0.001. After 10,000 iterations, the model reaches a stable state.

[0070] In one embodiment, before calculating the coal powder extraction rate and delivery speed using coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters, the coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters are filtered, denoised, and normalized. That is, the coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters used in calculating the coal powder extraction rate and delivery speed using the fuzzy control algorithm or neural network algorithm are all filtered, denoised, and normalized versions of these parameters.

[0071] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Existing technologies mostly employ fixed parameters or simple logic to control the coal powder intake and delivery process, making it difficult to cope with dynamic changes in coal powder characteristics, pipeline pressure, and conveying distance. This invention comprehensively considers multiple factors and utilizes artificial intelligence algorithms such as fuzzy control and neural networks to achieve dynamic optimization and adjustment of the coal powder intake and delivery speed. For example, given the differences in coal powder characteristics caused by coal from different origins, existing technologies may require manual adjustment of equipment parameters, resulting in slow response and low accuracy. The neural network algorithm of this invention can automatically learn new operating condition data to complete parameter optimization and adjustment, ensuring stable coal powder delivery and exhibiting far greater adaptability than traditional technologies.

[0072] 2. Traditional systems often rely on limited data acquisition, monitoring only a few key parameters and lacking in-depth data processing. This invention constructs a comprehensive data acquisition module covering characteristic parameters such as coal powder particle size distribution, humidity, and flowability, as well as data on multi-point pipeline pressure and conveying distance. The acquisition equipment boasts high accuracy and fast response. For data processing, techniques such as median filtering, Kalman filtering, and normalization are employed, combined with advanced algorithms to deeply mine the value of the data, providing strong support for precise control.

[0073] 3. Existing powder collection and delivery equipment has fixed operating parameters or a limited adjustment range, resulting in poor coordination between devices. The powder collection equipment of this invention employs a variable pitch screw design and a high-precision variable frequency motor, offering a wide range and high precision for powder collection adjustment. The powder delivery fan is equipped with a high-performance variable frequency speed control device, enabling rapid and precise airflow adjustment, and the air pressure can be optimized by adjusting the inlet guide vanes. Simultaneously, all devices operate collaboratively under the unified command of the intelligent control module. For example, when the unit load changes rapidly, the system of this invention can synchronously adjust the powder collection and delivery speed within a short time, greatly improving the system's response speed and operating efficiency.

[0074] Specific limitations regarding the multi-factor coordinated pulverized coal control method based on an external boiler pulverized coal silo can be found in the limitations of the multi-factor coordinated pulverized coal control system based on an external boiler pulverized coal silo mentioned above, and will not be repeated here. When applying the multi-factor coordinated pulverized coal control method based on an external boiler pulverized coal silo provided by this invention, it is not necessary to consider... Figure 1 The steps shown are executed in sequence. The specific execution order of each step can be determined as needed, and this invention does not impose any restrictions on it.

[0075] The above describes a multi-factor coordinated pulverized coal control method based on an external pulverized coal silo provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding multi-factor coordinated pulverized coal control device based on an external pulverized coal silo, the device comprising: The data acquisition module is used to collect coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters. The coal powder characteristic parameters include coal powder particle size distribution parameters, moisture parameters, and flowability parameters. The pipeline pressure parameters include coal powder intake pipeline pressure parameters and coal powder delivery pipeline pressure parameters. The conveying distance parameter is the length of the coal powder conveying path from the external coal silo to the boiler. The control module is used to calculate the amount of coal powder taken and the coal powder delivery speed based on the coal powder characteristic parameters, pipeline pressure parameters and conveying distance parameters, using fuzzy control algorithm or neural network algorithm. Based on the amount of coal powder taken, it controls the coal powder taking equipment to take coal powder from the external coal silo of the boiler, and based on the coal powder delivery speed, it controls the coal powder delivery fan to deliver the coal powder to the boiler.

[0076] Specific limitations regarding the multi-factor coordinated pulverized coal control device based on an external boiler pulverized coal silo can be found in the limitations of the multi-factor coordinated pulverized coal control method based on an external boiler silo mentioned above, and will not be repeated here. Each module in the aforementioned multi-factor coordinated pulverized coal control device based on an external boiler silo can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0077] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 A multi-factor collaborative pulverized coal control method based on an external pulverized coal silo for boilers is provided.

[0078] The present invention also provides Figure 3 The schematic diagram of the computer device shown is as follows: Figure 3 As shown, at the hardware level, this computer device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then executes it to achieve the above. Figure 1 A multi-factor collaborative pulverized coal control method based on an external pulverized coal silo for boilers is provided.

[0079] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0080] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this invention.

Claims

1. A multi-factor collaborative pulverized coal control system based on an external pulverized coal silo in a boiler, characterized in that, The system includes: a data acquisition module, an intelligent control module, a powder collection device, and a powder delivery fan; the intelligent control module is connected to the data acquisition module, the powder collection device, and the powder delivery fan respectively. The data acquisition module is used to collect coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters. The coal powder characteristic parameters include coal powder particle size distribution parameters, moisture parameters, and flowability parameters. The pipeline pressure parameters include coal powder intake pipeline pressure parameters and coal powder delivery pipeline pressure parameters. The conveying distance parameter is the length of the coal powder conveying path from the external coal silo to the boiler. The intelligent control module is used to calculate the amount of coal powder taken and the coal powder delivery speed based on the coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters through fuzzy control algorithms or neural network algorithms. Based on the amount of coal powder taken, it controls the coal powder taking equipment to take coal powder from the external coal silo of the boiler, and controls the coal powder delivery fan to deliver the coal powder to the boiler based on the coal powder delivery speed.

2. The system according to claim 1, characterized in that, The data acquisition module includes a sensor group for detecting coal powder characteristic parameters, a pressure sensor for detecting pipeline pressure parameters, and a positioning module for acquiring conveying distance parameters. The pressure sensors are installed at the inlet, middle, and outlet sections of the coal powder intake pipeline, as well as at the starting end, intermediate pressurization point, and boiler inlet of the coal powder delivery pipeline. The sensor group includes a particle size analyzer, a humidity sensor, and a rheometer for detecting the flowability of coal powder.

3. The system according to claim 1, characterized in that, The intelligent control module includes a data processing unit and an algorithm calculation unit; The data processing unit is used to filter, reduce noise, and normalize the collected coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters. The algorithm calculation unit incorporates fuzzy control algorithm and neural network algorithm, which are used to calculate the powder picking amount and powder delivery speed based on the processed data.

4. The system according to claim 1, characterized in that, The powder extraction equipment is a spiral powder extractor equipped with a variable frequency motor. The variable frequency motor adjusts its speed according to the instructions of the intelligent control module to control the amount of powder extracted. The powder extraction amount can be adjusted from 0 to 100%, and the adjustment accuracy is ±1%.

5. The system according to claim 1, characterized in that, The powder conveying fan is a centrifugal fan equipped with a variable frequency speed control device. The variable frequency speed control device adjusts the fan speed according to the instructions of the intelligent control module, so that the air volume of the powder conveying fan can be adjusted within the range of 1000~10000 m³ / h, and the response time is less than 1 s.

6. A control method for a multi-factor collaborative pulverized coal control system based on an external pulverized coal silo in a boiler, characterized in that, The method includes: Collect coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters; coal powder characteristic parameters include coal powder particle size distribution parameters, moisture parameters, and flowability parameters; pipeline pressure parameters include coal powder intake pipeline pressure parameters and coal powder delivery pipeline pressure parameters; conveying distance parameters are the coal powder conveying path length from the external coal powder silo to the boiler; Based on the pulverized coal characteristic parameters, pipeline pressure parameters, and conveying distance parameters, the pulverized coal extraction amount and conveying speed are calculated using fuzzy control algorithms or neural network algorithms. The extraction amount is used to control the pulverized coal extraction equipment to extract pulverized coal from the external pulverized coal silo of the boiler, and the conveying speed is used to control the conveying fan to transport the pulverized coal to the boiler.

7. The method according to claim 6, characterized in that, The powder dispensing amount and powder delivery speed are calculated using a fuzzy control algorithm, including: The pulverized coal characteristic parameters, pipeline pressure parameters, and conveying distance parameters are converted into fuzzy linguistic variables. Through preset fuzzy inference rules, the adjustment amounts of the operating parameters of the pulverized coal extraction equipment and the pulverized coal conveying fan are output.

8. The method according to claim 6, characterized in that, The powder intake and delivery speed are calculated using a neural network algorithm, including: By using a neural network algorithm to model the nonlinear mapping relationship between the amount of coal powder taken and the coal powder delivery speed and the coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters, the coal powder taking amount and delivery speed corresponding to the current coal powder characteristic parameters, pipeline pressure parameters, and conveying distance parameters are determined.

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