Soot blower automatic control system based on PLC, servo controller and servo motor

Abstract

The invention provides a soot blower automatic control system based on a PLC, a servo controller and a servo motor, and relates to the technical field of industrial equipment control. A driving module; a monitoring module; the soot blowing strategy decision module is used for collecting various data from the monitoring module; an intelligent decision module; the operation interface module is used for inputting various control instructions through an operation interface and checking the running state and fault information of the soot blower on the operation interface in real time; the power supply module is used for providing a stable power supply for the whole system; according to the device, a combined control mode of the PLC, the servo controller and the servo motor is adopted, the motion track, the speed and the soot blowing force of the soot blower can be accurately controlled, the soot blowing effect is improved, the PLC can adjust control instructions in real time according to the data, and the control accuracy is improved. And the soot blower is ensured to always run in an optimal state.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial equipment control, and particularly to an automatic soot blower control system based on a PLC, a servo controller, and a servo motor. Background Art

[0002] An automatic soot blower control system is an automated system used in industrial boilers, heat exchangers, and other equipment to remove accumulated ash. Accumulated ash can reduce heat exchange efficiency, increase energy consumption, and may even cause equipment failures. The main function of the soot blower is to blow the accumulated ash and slag on the heating surface of the boiler, and is used to remove the accumulated ash and slag on the water wall, superheater, reheater, and economizer, and can also be used to remove the accumulated ash on the furnace top and tubular air preheater. The soot blower is a commonly used device to solve this problem.

[0003] The traditional control method of the soot blower is as follows: The purge cycle starts when the soot blower gun is in the starting position. After the power is turned on, the motor drives the carriage to move forward along the guide rails on both sides of the beam, and the soot blower gun is sent into the boiler. After the nozzle enters the furnace, the carriage opens the valve and the soot blowing starts. The carriage continues to screw the soot blower gun into the boiler until it reaches the extreme front end, then the carriage reverses and guides the barrel to retreat along a different soot blowing trajectory from when it advanced. When the nozzle approaches the furnace wall, the valve closes and the soot blowing stops, and the carriage continues to retreat and returns to the starting position. This control method often cannot accurately control the movement trajectory and soot blowing intensity of the soot blower, and can only rely on simple and crude limited monitoring means for empirical judgment, resulting in poor soot blowing effect and inability to meet the requirements of efficient and stable operation of industrial production. Usually limited by its own accuracy and affected by external environmental factors such as temperature changes and power fluctuations, there may be problems such as low control accuracy, difficulty for monitoring personnel to make flexible adjustments according to actual working conditions, inability to accurately control the operation process of the soot blower, and it is easy to have the situation of over-soot blowing or insufficient soot blowing, affecting the soot blowing effect and the service life of the boiler equipment.

[0004] Therefore, it is necessary to provide a new automatic soot blower control system based on a PLC, a servo controller, and a servo motor to solve the above technical problems. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides an automatic soot blower control system based on a PLC, a servo controller, and a servo motor.

[0006] The automatic soot blower control system based on a PLC, a servo controller, and a servo motor provided by the present invention includes a PLC control module for storing a control program and generating a control instruction according to equipment working condition parameters and a preset program;

[0007] A drive module for receiving a control signal sent by the PLC control module and converting it into a drive signal;

[0008] The monitoring module is used to monitor the telescopic position information of the soot blower, and feedback it to the PLC control module, and monitor the temperature of the working object, the pressure and flow parameters of the soot blowing medium, and at the same time monitor the drive module;

[0009] The soot blowing strategy decision-making module is used to collect various data from the monitoring module, and predict the soot blowing working conditions and evaluate the soot blowing effect;

[0010] The intelligent decision-making module is used to receive the data generated by the monitoring module and the soot blowing strategy decision-making module, and at the same time conduct decision-making analysis on the data generated by the monitoring module and the soot blowing strategy decision-making module through an adaptive control algorithm. And during the decision-making analysis process, it will flexibly adjust the analysis weight and decision-making logic according to the dynamic change characteristics of the data and the current actual operating conditions of the system, and it is electrically connected to the monitoring module and the soot blowing strategy decision-making module respectively;

[0011] The operation interface module is used to input various control instructions through the operation interface, and view the running status and fault information of the soot blower in real time on the operation interface;

[0012] The power supply module is used to provide stable power for the entire system;

[0013] The communication module is used to communicate with other devices or the upper computer.

[0014] Preferably, the decision-making analysis of the data generated by the monitoring module and the soot blowing strategy decision-making module through the adaptive control algorithm specifically includes the following steps:

[0015] S1. Data collection, collect the data of the equipment running status, the historical data, current plan and execution situation of the soot blowing operation from the monitoring module and the soot blowing strategy decision-making module respectively;

[0016] S2. Data preprocessing, first remove the noise, outliers or incomplete data records, and at the same time convert the data of different scales into a unified standard;

[0017] S3. Feature extraction and status evaluation, extract the parameters related to the soot blowing effect, and judge the current equipment status according to the extracted data, and establish a mathematical / statistical model of the soot blowing process and equipment performance;

[0018] S4. Execution of the adaptive control algorithm, use the recursive least squares method to update the model parameters in real time, compare the actual monitoring data with the ideal reference model, generate an error signal, and finally dynamically adjust the control strategy according to the error;

[0019] S5. Multi-objective optimization and decision-making generation, balance among energy consumption, equipment life, and thermal efficiency, set the optimization objective function, and use the genetic algorithm to find the optimal sootblowing timing and intensity. Meanwhile, dynamically correct the threshold for triggering sootblowing according to the real-time working conditions;

[0020] S6. Strategy execution and closed-loop feedback, send the optimized sootblowing strategy value to the PLC control module, and coordinate the operation of multiple sootblowers through the PLC system, and then conduct effect feedback and error correction;

[0021] S7. Algorithm iteration and self-learning optimization, establish a sootblowing effect database, record the working conditions, strategy parameters, and effect indicators of each purge, and form a "working condition - strategy - effect" mapping table, and conduct regular offline training at the same time.

[0022] Preferably, the specific steps of using the genetic algorithm to find the optimal sootblowing timing and intensity in S5 are as follows;

[0023] S51. Problem modeling and parameter definition, clarify the optimization objective, determine the objective function to be optimized, define the constraints of the problem, and at the same time use the sootblowing timing and intensity as optimization variables;

[0024] S52. Coding scheme, encode each possible solution as a chromosome, and adopt binary coding. Binary coding: use 0 and 1 to represent different states of the sootblowing timing and intensity;

[0025] S53. Initialize the population, randomly generate a set of initial solutions, each solution corresponds to a chromosome, and the population size needs to be set according to the problem complexity;

[0026] S54. Define the fitness function, construct the fitness function according to the optimization objective, used to evaluate the quality of each individual, and at the same time ensure that the fitness values are comparable and avoid being too large or too small;

[0027] S55. Genetic operations, selection, select excellent individuals according to the fitness values to enter the next generation, and crossover, exchange part of the genes of two parent individuals to generate new offspring individuals. At the same time, mutation, randomly change part of the genes of the individual to introduce new solution space exploration;

[0028] S56. Iterative evolution, repeat the selection, crossover, and mutation operations in the genetic operations until the termination condition is met;

[0029] S57. Result output and verification, select the individual with the highest fitness from the final population, decode it into the actual sootblowing timing and intensity, and conduct verification.

[0030] Preferably, the driving module includes a servo controller and a servo motor. The servo controller is electrically connected to the PLC control module and is used for decoding and processing the control instruction and outputting a driving signal to the servo motor. The servo motor is connected to the mechanical transmission mechanism of the soot blower and is used for driving the soot blowing gun to operate along a set track.

[0031] Preferably, the monitoring module includes a position sensor, a pressure sensor, and a flow sensor. The position sensor is used for monitoring the telescopic position of the soot blowing gun. The pressure sensor and the flow sensor are used for monitoring the pressure and flow parameters of the soot blowing medium. The monitoring module feeds back the real-time monitoring data to the PLC control module and the intelligent decision-making module.

[0032] Preferably, the operation interface module includes a human-machine interaction interface, which supports manual input of control instructions, presetting of soot blowing strategy parameters, and real-time display of the operation status data, fault alarm information, and historical operation records of the soot blower.

[0033] Preferably, the soot blowing strategy decision-making module establishes a soot blowing effect evaluation model based on historical data and real-time monitoring data, predicts the degree of ash accumulation and the purging requirement through fuzzy logic or machine learning algorithms, and outputs soot blowing strategy suggestions to the intelligent decision-making module.

[0034] Preferably, the parameters related to the soot blowing effect include soot blowing medium parameters, soot blowing equipment operation parameters, equipment operation environment parameters, effect evaluation and optimization parameters, and special parameters of other soot blowing methods.

[0035] Compared with the related technologies, the automatic control system for a soot blower provided by the present invention based on a PLC, a servo controller, and a servo motor has the following beneficial effects:

[0036] 1. Through the PLC control module and the driving module provided in the present invention, the PLC control module generates corresponding control instructions according to the equipment working condition parameters collected by the sensor unit and the preset control program, and sends them to the servo controller. After receiving the instructions, the servo controller decodes and processes the instructions, adjusts the output control signal, drives the servo motor to rotate, and the servo motor drives the mechanical transmission mechanism of the soot blower, so that the soot blower operates along the set action track to complete the soot blowing operation. At the same time, the sensor unit real-time monitors the operation status of the soot blower and the equipment working condition parameters, and feeds back this information to the PLC control unit. By adopting the combined control method of a PLC, a servo controller, and a servo motor, this device can accurately control the movement track, speed, and soot blowing force of the soot blower, improve the soot blowing effect, and the PLC can adjust the control instructions in real time according to these data to ensure that the soot blower always operates in the best state.

[0037] 2. The present invention uses an adaptive control algorithm to perform decision analysis on data. First, it collects data on the operating status of the equipment, historical data on soot blowing operations, current plans, and execution status from the monitoring module and the soot blowing strategy decision module. Then, it preprocesses the collected data to remove noise, outliers, or incomplete records, and converts data of different scales into a unified standard for subsequent processing and comparison. After preprocessing, feature extraction and status evaluation are carried out to identify parameters closely related to the soot blowing effect, and the current equipment status is evaluated based on the extracted data. At the same time, optimization functions are set for multiple objectives such as energy consumption, equipment life, and thermal efficiency, and a genetic algorithm is used to find the optimal soot blowing timing and intensity. The optimized soot blowing strategy is sent to the PLC control module, and the PLC system coordinates the operation of multiple soot blowers to implement specific soot blowing operations. This device reduces the probability of problems such as low control accuracy, difficulty for monitoring personnel to make flexible adjustments according to actual working conditions, and inability to accurately control the operation process of the soot blower, and also reduces the probability of over-soot blowing or insufficient soot blowing, reducing the impact on the soot blowing effect and equipment life. Description of the Drawings

[0038] Figure 1 is the structural block diagram of the automatic control system for a soot blower based on a PLC, a servo controller, and a servo motor provided by the present invention;

[0039] Figure 2 is the flow block diagram of using an adaptive control algorithm to perform decision analysis on data provided by the present invention;

[0040] Figure 3 is the flow block diagram of using a genetic algorithm to find the optimal soot blowing timing and intensity provided by the present invention;

[0041] Figure 4 is the structural schematic diagram of the automatic control system for a soot blower based on a PLC, a servo controller, and a servo motor provided by the present invention. Detailed Embodiments

[0042] The present invention will be further described below in conjunction with the drawings and embodiments.

[0043] Please refer to Figure 1 , Figure 2 , Figure 3 and Figure 4 , where Figure 1 is the structural block diagram of the automatic control system for a soot blower based on a PLC, a servo controller, and a servo motor provided by the present invention; Figure 2 is the flow block diagram of using an adaptive control algorithm to perform decision analysis on data provided by the present invention; Figure 3 is the flow block diagram of using a genetic algorithm to find the optimal soot blowing timing and intensity provided by the present invention;Figure 4 This is a schematic diagram of the automatic control system for soot blowers based on PLC, servo controller, and servo motor provided by the present invention.

[0044] In the specific implementation process, as Figures 1 to 4 shown, the automatic control system for soot blowers based on PLC, servo controller, and servo motor includes a PLC control module, which is used to store control programs and generate control instructions according to equipment working condition parameters and preset programs;

[0045] It should be noted that the main functions of the PLC control module include data acquisition and processing, logical control and decision-making, instruction output and execution, as well as fault diagnosis and alarm. Among them, data acquisition and processing: receive various data from the monitoring module, such as the telescopic position information of the soot blower gun, the pressure and flow parameters of the soot blowing medium, and the operating status of the drive module, and perform preprocessing on the acquired data, including filtering, amplification, and linearization operations. At the same time, store the processed data in the internal memory of the PLC;

[0046] Logical control and decision-making: According to the preset control program and the acquired data, perform logical judgment and decision-making to implement various control logics of the soot blower, such as sequential control, interlock control, and protection control;

[0047] Instruction output and execution: According to the results of logical judgment and decision-making, output control instructions to the drive module to control the operation of the servo motor, realize the telescopic and rotational movements of the soot blower gun, and perform real-time monitoring and feedback on the operating status of the drive module. Adjust the control instructions in a timely manner according to the feedback information to ensure the operation accuracy and stability of the soot blower;

[0048] Fault diagnosis and alarm: Real-time monitor the operating status of the system, diagnose and alarm various faults, and record the fault information and occurrence time for subsequent fault analysis and processing;

[0049] Moreover, the working process of the PCL control module includes the following steps:

[0050] Step 1: Initialization. When the system starts, the PLC control module performs initialization operations;

[0051] Step 2: Data acquisition. Through the input interface, real-time collect various data from the monitoring module and store them in the internal memory of the PLC;

[0052] Step 3: Logic processing. According to the preset control program and the acquired data, perform logical judgment and decision-making to generate corresponding control instructions;

[0053] Step 4: Instruction output. Through the output interface, send the control instructions to the drive module to control the operation of the servo motor and realize various actions of the soot blower;

[0054] Step Five, Status Monitoring and Feedback: Monitor the operating status of the drive module and other parameters of the system in real time, compare the feedback information with the preset target values, and adjust the control instructions according to the comparison results to achieve closed-loop control;

[0055] Step Six, Fault Diagnosis and Handling: During the operation of the system, monitor the operating status of the system in real time, diagnose and alarm various faults;

[0056] Step Seven, Loop Execution: Repeat the above Steps 2 - 6 continuously to achieve real-time control and monitoring of the soot blower;

[0057] The drive module is used to receive the control signal sent by the PLC control module and convert it into a drive signal;

[0058] It should be noted that the drive module includes a servo controller and a servo motor. The servo controller is electrically connected to the PLC control module, used to decode and process the control instructions, and output a drive signal to the servo motor. The servo motor is connected to the mechanical transmission mechanism of the soot blower and is used to drive the soot blowing gun to run according to the set trajectory;

[0059] Servo Controller: As the "brain" of the drive module, it receives the control instructions from the PLC control module, decodes, processes, and calculates the instructions, and then outputs a suitable drive signal to the servo motor. At the same time, it also has the function of monitoring and feedback on the operating status of the servo motor, and can adjust the drive signal in real time to ensure the accuracy and stability of the motor operation;

[0060] Servo Motor: It is the actuator of the drive module and is connected to the mechanical transmission mechanism of the soot blower. It converts electrical energy into mechanical energy according to the drive signal output by the servo controller, drives the soot blowing gun to perform actions such as telescoping and rotating according to the set trajectory, and realizes the purging operation of the boiler heating surface;

[0061] The monitoring module is used to monitor the telescopic position information of the soot blower and feedback it to the PLC control module, and monitor the temperature of the working object, the pressure and flow parameters of the soot blowing medium, and at the same time monitor the drive module;

[0062] It should be noted that the monitoring module includes a position sensor, a pressure sensor, a flow sensor, and a temperature sensor. The position sensor is used to monitor the telescopic position of the soot blowing gun, the pressure sensor and the flow sensor are used to monitor the temperature of the working object, the pressure and flow parameters of the soot blowing medium, and the monitoring module feeds the real-time monitoring data back to the PLC control module and the intelligent decision-making module;

[0063] Position sensors, usually encoders, magnetic scales, etc., are installed on the drive mechanism of the soot blower gun to monitor the telescopic position and rotation angle of the soot blower gun in real time, ensuring that the soot blower gun can accurately reach the preset purging position;

[0064] Pressure sensors are generally installed on the conveying pipeline of the soot blowing medium to measure the pressure of the soot blowing medium (such as steam, compressed air), ensuring that the pressure of the soot blowing medium is stabilized within a suitable range to achieve a good soot blowing effect;

[0065] Flow sensors are also installed on the conveying pipeline of the soot blowing medium to monitor the flow rate of the soot blowing medium. By monitoring the flow rate, it can be judged whether the supply of the soot blowing medium is sufficient and whether there are problems such as leakage;

[0066] Each sensor in the monitoring module measures the corresponding physical quantity according to its working principle and converts the measurement result into an electrical signal. These electrical signals are processed through amplification, filtering, etc. and then transmitted to the PLC control module through the analog input module or digital input module. The PLC control module collects, processes, and analyzes these signals, converts them into actual physical quantity values, and compares them with the preset parameter range to judge whether the operation state of the system is normal;

[0067] The soot blowing strategy decision-making module is used to collect various data from the monitoring module and predict the soot blowing working conditions and evaluate the soot blowing effect;

[0068] The soot blowing strategy decision-making module in the automatic control system of the soot blower based on PLC, servo controller, and servo motor is the core hub connecting the monitoring data and control execution. This module realizes the accurate prediction of soot blowing requirements and strategy optimization by integrating real-time working conditions, historical data, and algorithm models, solving the blindness problem of traditional experience control;

[0069] Module positioning and core functions, real-time data of the monitoring module: soot blower gun position, soot blowing medium pressure / flow rate / temperature, heating surface wall temperature / pressure difference, boiler load, fuel type;

[0070] Historical soot blowing records: purging pressure, duration, path, effect evaluation results;

[0071] Among them, the key algorithms and functions include: fouling prediction model, multi-objective optimization algorithm, and soot blowing path planning algorithm;

[0072] The fouling prediction model describes the deposition, adhesion, and growth laws of ash particles on the heating surface by establishing a physical and chemical model of the fouling process. Its calculation formula is:

[0073]

[0074] Among them, R(t) is the fouling thermal resistance at time t, R0 is the initial heat value, k is the fouling coefficient, C(r) is the concentration of ash particles in the flue gas flow, is the inhibitory effect of the flue gas flow velocity v on fouling;

[0075] The intelligent decision-making module is used to receive the data generated by the monitoring module and the soot blowing strategy decision-making module. At the same time, it conducts decision-making analysis on the data generated by the monitoring module and the soot blowing strategy decision-making module through an adaptive control algorithm. And during the decision-making analysis process, it will flexibly adjust the analysis weight and decision-making logic according to the dynamic change characteristics of the data and the current actual operating conditions of the system. And it is electrically connected to the monitoring module and the soot blowing strategy decision-making module respectively;

[0076] It should be noted that the decision-making analysis of the data generated by the monitoring module and the soot blowing strategy decision-making module through the adaptive control algorithm specifically includes the following steps:

[0077] S1. Data collection, collect the data of the equipment operating status, as well as the historical data, current plan and execution situation related to the soot blowing operation from the monitoring module and the soot blowing strategy decision-making module respectively;

[0078] S2. Data preprocessing, first remove the noise, outliers or incomplete data records, and at the same time convert the data of different scales into a unified standard;

[0079] S3. Feature extraction and status evaluation, extract the parameters related to the soot blowing effect, and judge the current equipment status according to the extracted data, and establish a mathematical / statistical model of the soot blowing process and equipment performance;

[0080] S4. Execution of the adaptive control algorithm, use the recursive least squares method to update the model parameters in real time, compare the actual monitoring data with the ideal reference model, generate an error signal, and finally dynamically adjust the control strategy according to the error;

[0081] S5. Multi-objective optimization and decision-making generation, balance among energy consumption, equipment life, and thermal efficiency, set the optimization objective function, and use the genetic algorithm to find the optimal soot blowing timing and intensity. At the same time, dynamically correct the threshold for triggering soot blowing according to the real-time working conditions;

[0082] It should be noted that the specific steps of using the genetic algorithm to find the optimal soot blowing timing and intensity in S5 are as follows;

[0083] S51. Problem modeling and parameter definition, clarify the optimization objective, determine the objective function to be optimized, define the constraints of the problem, and at the same time take the soot blowing timing and intensity as the optimization variables;

[0084] S52. Coding Scheme: Encode each possible solution as a chromosome and adopt binary coding. Binary coding: Use 0 and 1 to represent different states of soot blowing timing and intensity;

[0085] S53. Initialize the population: Randomly generate a set of initial solutions, with each solution corresponding to a chromosome. The population size needs to be set according to the problem complexity;

[0086] S54. Define the fitness function: Construct a fitness function based on the optimization objective to evaluate the quality of each individual. At the same time, ensure that the fitness values are comparable and avoid being too large or too small;

[0087] S55. Genetic operations: Selection: Select excellent individuals according to the fitness values to enter the next generation. Crossover: Exchange some genes of two parent individuals to generate new offspring individuals. Mutation: Randomly change some genes of an individual to introduce exploration of a new solution space;

[0088] S56. Iterative evolution: Repeat the selection, crossover, and mutation operations in the genetic operations until the termination condition is met;

[0089] S57. Result output and verification: Select the individual with the highest fitness from the final population, decode it into the actual soot blowing timing and intensity, and conduct verification;

[0090] S6. Strategy execution and closed-loop feedback: Send the optimized soot blowing strategy value to the PLC control module and coordinate the operation of multiple soot blowers through the PLC system. Then, conduct effect feedback and error correction;

[0091] S7. Algorithm iteration and self-learning optimization: Establish a soot blowing effect database, record the working conditions, strategy parameters, and effect indicators of each purge, and form a "working condition - strategy - effect" mapping table. At the same time, conduct regular offline training;

[0092] The operation interface module is used to input various control instructions through the operation interface and view the running status and fault information of the soot blower in real time on the operation interface;

[0093] It should be noted that the functions of the operation interface module include real-time monitoring function, control operation function, historical data query and analysis function, and system management function. Among them, the real-time monitoring function: Device status display: Show the running status of the soot blower (such as purging, stopping, malfunction, etc.), parameters such as the rotation speed, temperature, and current of the servo motor, as well as the working mode and status information of the servo controller;

[0094] Control operation function: Manual operation or automatic operation can be selected. Manual control: The operator can manually control the start, stop, forward, backward and other actions of the soot blower through the interface, as well as adjust the rotation speed of the servo motor and the parameters of the soot blowing medium. Automatic control: Select the preset automatic soot blowing mode, and the system will automatically complete the soot blowing task according to the set program;

[0095] Historical data query and analysis function: The system automatically records the operation data, process parameters and alarm information of the soot blower to form a historical database. The operator can query the historical data according to conditions such as time range, equipment name, parameter type, etc., and display it in the form of a table or chart;

[0096] Power supply module, used to provide stable power supply for the whole system;

[0097] It should be noted that the power supply module in the automatic control system of the soot blower based on PLC, servo controller and servo motor is the basis for ensuring the stable operation of the system. Its core role is to provide safe, reliable and pure power supply for the PLC control unit, servo controller, servo motor, sensors and various electronic components;

[0098] Communication module, used to communicate with other devices or the upper computer;

[0099] It should be noted that the communication module in the automatic control system of the soot blower based on PLC, servo controller and servo motor is a key component for realizing internal data interaction of the system and linkage with external devices. Its core role is to ensure seamless communication between devices such as the PLC control module, servo system, sensors and the upper computer, and support the automated and intelligent operation of the system.

[0100] The circuits and controls involved in the present invention are all prior arts and will not be elaborated here.

[0101] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. An automatic soot blower control system based on a PLC, a servo controller, and a servo motor, characterized in that, It includes a PLC control module for storing control programs and generating control instructions according to equipment operating condition parameters and preset programs; A drive module for receiving control signals sent by the PLC control module and converting them into drive signals; A monitoring module for monitoring the telescopic position information of the soot blower and feeding it back to the PLC control module, and monitoring parameters such as the temperature of the working object, the pressure and flow rate of the soot blowing medium, and simultaneously monitoring the drive module; A soot blowing strategy decision-making module for collecting various data from the monitoring module, predicting the soot blowing condition, and evaluating the soot blowing effect; An intelligent decision-making module for receiving data generated by the monitoring module and the soot blowing strategy decision-making module, and simultaneously performing decision analysis on the data generated by the monitoring module and the soot blowing strategy decision-making module through an adaptive control algorithm. During the decision analysis process, it will flexibly adjust the analysis weight and decision logic based on the dynamic change characteristics of the data and the current actual operating condition of the system, and it is electrically connected to the monitoring module and the soot blowing strategy decision-making module respectively; An operation interface module for inputting various control instructions through the operation interface and viewing the operating status and fault information of the soot blower in real time on the operation interface; A power supply module for providing stable power for the entire system; A communication module for communicating with other devices or the upper computer.

2. The soot blower automatic control system based on a PLC, a servo controller, and a servo motor according to claim 1, wherein The decision analysis of the data generated by the monitoring module and the soot blowing strategy decision-making module through the adaptive control algorithm specifically includes the following steps: S1. Data collection, collecting data on the operating status of the equipment, historical data on soot blowing operations, current plans and execution status from the monitoring module and the soot blowing strategy decision-making module respectively; S2. Data preprocessing, first removing noise, outliers or incomplete data records, and at the same time converting data of different scales into a unified standard; S3. Feature extraction and status evaluation, extracting parameters related to the soot blowing effect, judging the current equipment status based on the extracted data, and establishing a mathematical / statistical model of the soot blowing process and equipment performance; S4. Execution of the adaptive control algorithm, using the recursive least squares method to update the model parameters in real time, comparing the actual monitoring data with the ideal reference model to generate an error signal, and finally dynamically adjusting the control strategy according to the error; S5. Multi-objective optimization and decision generation, balancing among energy consumption, equipment life, and thermal efficiency, setting an optimization objective function, and using the genetic algorithm to find the optimal soot blowing timing and intensity, and dynamically correcting the threshold for triggering soot blowing according to the real-time operating condition; S6. Strategy execution and closed-loop feedback, sending the optimized soot blowing strategy value to the PLC control module, coordinating the operation of multiple soot blowers through the PLC system, and then performing effect feedback and error correction; S7. Algorithm iteration and self-learning optimization, establishing a soot blowing effect database, recording the operating conditions, strategy parameters, and effect indicators of each purge, forming a "operating condition - strategy - effect" mapping table, and simultaneously performing regular offline training.

3. The soot blower automatic control system based on a PLC, a servo controller, and a servo motor according to claim 2, wherein The specific steps for using the genetic algorithm to find the optimal soot blowing timing and intensity in S5 are as follows; S51. Problem modeling and parameter definition, clarify the optimization goal, determine the objective function to be optimized, define the constraints of the problem, and at the same time take the soot blowing timing and intensity as optimization variables; S52. Coding scheme, encode each possible solution as a chromosome, and adopt binary coding. Binary coding: use 0 and 1 to represent different states of the soot blowing timing and intensity; S53. Initialize the population, randomly generate a set of initial solutions, each solution corresponds to a chromosome, and the population size needs to be set according to the problem complexity; S54. Define the fitness function, construct the fitness function according to the optimization goal, which is used to evaluate the quality of each individual. At the same time, ensure that the fitness values are comparable and avoid being too large or too small; S55. Genetic operations, selection, select excellent individuals according to the fitness values to enter the next generation, and crossover, exchange part of the genes of two parent individuals to generate new offspring individuals. At the same time, mutation, randomly change part of the genes of an individual to introduce exploration of a new solution space; S56. Iterative evolution, repeat the selection, crossover and mutation operations in the genetic operations until the termination condition is met; S57. Result output and verification, select the individual with the highest fitness from the final population, decode it into the actual soot blowing timing and intensity, and conduct verification.

4. The soot blower automatic control system based on a PLC, a servo controller, and a servo motor according to claim 3, wherein, The driving module includes a servo controller and a servo motor. The servo controller is electrically connected to the PLC control module, and is used to decode and process the control instruction, and output a driving signal to the servo motor. The servo motor is connected to the mechanical transmission mechanism of the soot blower, and is used to drive the soot blowing gun to run according to the set trajectory.

5. The soot blower automatic control system based on a PLC, a servo controller, and a servo motor according to claim 4, wherein The monitoring module includes a position sensor, a pressure sensor, a flow sensor, and a temperature sensor. The position sensor is used to monitor the telescopic position of the soot blowing gun. The pressure sensor and the flow sensor are used to monitor the pressure and flow parameters of the soot blowing medium. The monitoring module feeds back the real-time monitoring data to the PLC control module and the intelligent decision-making module.

6. The automatic soot blower control system based on a PLC, a servo controller, and a servo motor according to claim 5, characterized in that, The operation interface module includes a human-machine interface, which supports manual input of control instructions, presetting of soot blowing strategy parameters, and real-time display of the running state data, fault alarm information and historical running records of the soot blower.

7. The soot blower automatic control system based on a PLC, a servo controller, and a servo motor according to claim 6, wherein, The soot blowing strategy decision-making module establishes a soot blowing effect evaluation model based on historical data and real-time monitoring data, predicts the degree of fouling and the blowing demand through fuzzy logic or machine learning algorithms, and outputs soot blowing strategy suggestions to the intelligent decision-making module.

8. The automatic soot blower control system based on a PLC, a servo controller, and a servo motor according to claim 7, wherein The parameters related to the soot blowing effect include soot blowing medium parameters, soot blowing equipment operation parameters, equipment operation environment parameters, effect evaluation and optimization parameters, and special parameters of other soot blowing methods.

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