Intelligent anti-blocking and energy-saving full-pipe conveying system for pneumatic ash conveying pipeline

Abstract

The application discloses a kind of intelligent anti-blocking energy-saving full pipe conveying system of pneumatic ash conveying pipeline, it relates to pneumatic ash conveying equipment technical field, the system includes: working condition acquisition module is along the sensing unit of ash conveying pipeline layout, acquires multiple data and processes and forms working condition information set;Bus transmission module constructs distributed network, realizes uplink and downlink data transmission interaction;Game decision module obtains information and calculates risk equivalent, generates control instruction;Fluidization adjustment module executes air supplementing etc. according to instruction and returns data;Safety guarantee module compares data in real time, executes protection and monitors state;The application lays out sensing unit, integrates multidimensional data, builds distributed network, generates accurate control instruction by algorithm, identifies hidden danger in real time, optimizes strategy, improves operation continuity and efficiency;It also uses different air supplementing, closed-loop control, sets independent safety module, strengthens stability and safety, prolongs component life, reduces operation difficulty and cost.

Description

Technical Field

[0001] This invention relates to the field of pneumatic ash conveying equipment technology, specifically to an intelligent anti-clogging and energy-saving full-pipe conveying system for pneumatic ash conveying pipelines. Background Technology

[0002] Pneumatic ash conveying is a core technology for fly ash treatment and material transportation in industries such as thermal power. Pipeline transportation has become the mainstream application in the industry due to its advantages such as high transportation efficiency and good environmental protection. Its operational stability is directly related to the continuous operation of industrial units, environmental emission control, and overall production energy consumption. With the continuous expansion of industrial production scale and the increasing requirements for intelligent development, the operating conditions of ash conveying systems exhibit complex characteristics of multi-factor coupling. Various conditions such as pipeline operating status, material properties, gas source parameters, and unit operating load will directly affect the conveying effect. The industry has put forward more stringent standards for the perception of operating conditions, dynamic control, energy consumption optimization, and safety protection of the ash conveying process. Achieving accurate control of the entire operating status of the ash conveying system, efficient data transmission, and intelligent optimization of control strategies have become key directions for promoting the upgrading of pneumatic ash conveying technology and meeting the needs of improving the quality and efficiency of industrial production. It is also a key technology that urgently needs to be broken through in the current material transportation field.

[0003] Traditional pneumatic ash conveying systems generally adopt a fixed operation and control mode, lacking comprehensive collection and in-depth fusion analysis of multi-dimensional operating condition information. They cannot accurately identify the internal operating status and potential faults of the pipeline in real time. The control logic relies heavily on manual experience, making it difficult to respond flexibly and adaptably to changes in real-time operating conditions. The differences in flow field characteristics in different areas of the pipeline are not specifically considered, and the gas replenishment operation and gas volume adjustment lack scientific basis, which can easily lead to problems such as material deposition and poor conveying inside the pipeline. This not only affects the conveying efficiency but also increases the probability of pipeline blockage. At the same time, the energy consumption control capability of traditional systems is weak, and the matching degree between the conveyed gas volume and the actual conveying demand is insufficient, resulting in excessive energy consumption. Equipment also suffers additional wear and tear due to unreasonable operation. The safety protection module is highly coupled with the original system, with insufficient independent protection capability. When power supply and communication are abnormal, it is not possible to quickly switch the operating mode. The control effect of safety risks such as ash dumping is poor. The overall system has many shortcomings such as poor operating stability, high energy consumption, high operation and maintenance costs, and insufficient safety protection, making it unable to meet the comprehensive requirements of modern industrial production. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent anti-clogging and energy-saving full-pipe conveying system for pneumatic ash conveying pipelines. This system comprises a closed-loop control system consisting of modules for operational condition acquisition, bus transmission, game theory decision-making, fluidization adjustment, and safety assurance. The system integrates multi-dimensional operational data through a global operational condition fusion algorithm and achieves intelligent decision-making based on a multi-objective game cost algorithm and a gradient fluidization optimal output algorithm. It implements differentiated gas replenishment and precise gas volume adjustment for different areas of the pipeline, and is equipped with an independently decoupled safety assurance module to complete pressure protection and mode switching. This invention effectively solves the problems of weak operational condition perception, delayed control, easy pipe blockage, high energy consumption, and insufficient safety protection in traditional ash conveying systems. It achieves stable full-pipe conveying, improves conveying efficiency and operational reliability, reduces energy consumption and equipment wear, and is suitable for industrial pneumatic ash conveying scenarios, possessing good practical value and promising prospects for promotion.

[0005] To solve the aforementioned technical problem, the present invention provides the following technical solution: an intelligent anti-clogging and energy-saving full-pipe conveying system for pneumatic ash conveying pipelines, the system comprising: Operating condition acquisition module: Pressure sensing units are deployed along the ash conveying pipeline and communicate with the ash content detection device, load monitoring unit, flow meter, and air compressor parameter terminal to collect pipeline pressure, air source pressure, coal ash content, unit load, conveying air flow rate, and air compressor output data, and calculate the ash-to-air ratio. After anti-interference and calibration processing, the data are integrated into a system operating condition status information dataset through a full-domain operating condition fusion algorithm. The system operating condition status information dataset includes the pressure values ​​at each location of the ash conveying pipeline and the pressure value of the supplementary gas source collected by the pressure sensing unit, the real-time coal ash content value collected by the ash content detection device, the real-time load value of the unit collected by the load monitoring unit, the instantaneous flow rate value of the conveying gas collected by the flow meter, the air compressor output value collected by the air compressor parameter terminal, and the system global fusion status value calculated by the global operating condition fusion algorithm. Bus transmission module: It adopts DC48V bus power supply and prefabricated quick connectors to build a distributed network. The uplink transmits the system operating status information dataset output by the operating condition acquisition module, and the downlink transmits the control commands output by the game decision module to the execution terminal. The interaction is realized by using a full-duplex communication protocol. Game Theory Decision Module: It obtains system operating status information and execution status data returned by fluidization regulation module through bus transmission module, calculates pipeline blockage risk value, transport energy consumption value and ash gas ratio matching degree using multi-objective game cost algorithm, and generates regulating valve opening command and gas replenishment valve action timing command through gradient fluidization optimal output algorithm, and sends the control command to bus transmission module. Fluidization control module: It obtains control commands from the bus transmission module, performs fixed-point air replenishment in the horizontal bend area, performs gradient air replenishment in the straight pipe section area, adjusts the total air delivery volume through the intelligent regulating valve, and transmits the regulating valve opening, air replenishment valve status, and flow field status data back to the bus transmission module as execution status data. Safety module: Real-time comparison of pipeline pressure and gas source pressure data, implementation of anti-ash spillage linkage protection, adopts an external structure decoupled from the original system, monitors power supply and communication status and switches operating modes; The operating condition acquisition module, game decision-making module, and fluidization adjustment module are sequentially connected through the bus transmission module to form a closed loop; the safety assurance module independently monitors and intervenes in the system operation.

[0006] Furthermore, the pressure sensing unit of the operating condition acquisition module is installed every 3 meters along the straight section of the economizer ash conveying pipeline, every 5 meters along the straight section of the electric field ash conveying pipeline, 0.5 meters before and after the horizontal bend, 1 meter after the discharge valve of the silo pump, and 2 units at the junction of the ash conveying branch pipe and the main pipe; the ash content detection device is connected to the inlet of the ash conveying pipeline, the load monitoring unit is connected to the unit's DCS system, the flow meter is installed on the main gas conveying pipeline near the outlet of the silo pump, and the air compressor parameter terminal is connected to the air compressor control cabinet.

[0007] Furthermore, in the aforementioned operating condition acquisition module, the mathematical expression for the full-domain operating condition fusion algorithm is:

[0008] in, This represents the system's overall fusion status value. For the weighting coefficient of pipeline pressure distribution, Weighted coefficient of coal quality entering the furnace, For unit load weighting factor, The weighting coefficients are for the gas flow rate, and all weighting coefficients are dimensionless coefficients ranging from 0 to 1, and satisfy the following conditions: The parameter is the arithmetic mean of the pressure data collected from all points along the ash conveying pipeline, after being normalized using the Min-Max method. The parameter is the real-time ash content value output by the coal ash content detection device after Min-Max normalization. The parameters are the real-time power generation load values ​​output by the unit load monitoring unit after Min-Max normalization. The parameter is the real-time instantaneous flow rate value output by the gas flow meter after Min-Max normalization.

[0009] Furthermore, the distributed network of the bus transmission module covers all monitoring points and execution terminals of the ash conveying pipeline; the full-duplex communication protocol undertakes data uplink and command downlink tasks respectively, realizing synchronous bidirectional full-duplex interaction.

[0010] Furthermore, in the game decision-making module, the execution status data includes the real-time opening degree of the regulating valve, the on / off status of the air supply valve, and the pipeline flow field status data. The system operating status information includes the original data collected from each monitoring point and the data processed by the global operating condition fusion algorithm. The regulating valve opening command includes the opening setting value of each regulating valve in the ash conveying pipeline. The generated air supply valve action timing command includes the start time, action interval duration, continuous action duration, and action start / stop sequence of the air supply valve in the horizontal bend area and the straight pipe section area.

[0011] Furthermore, the mathematical expression for the multi-objective game cost algorithm of the game decision module is as follows:

[0012] in, This represents the total game cost of the system. To prevent congestion, an adaptive weighting coefficient is used. This is a real-time value representing the risk and cost of pipe blockage calculated based on pipeline pressure distribution. An adaptive weighting coefficient for energy-saving costs. This is a real-time energy consumption cost calculated based on the air flow rate and air compressor output. An adaptive weighting coefficient for equipment wear cost. This is a real-time equipment wear cost value calculated based on the frequency of air replenishment valve operation and the change in the opening degree of the regulating valve.

[0013] Furthermore, the mathematical expression for the gradient fluidization optimal output algorithm of the game decision module is:

[0014] in, Output values ​​for the optimal action to be performed. The system state coupling coefficient is... The system global fusion state value output by the global operating condition fusion algorithm. The cost index is the suppression coefficient. This represents the total system game cost value output by the multi-objective game cost algorithm. This is the pipeline gradient fluidization correction factor. This is the correction value for pipeline gradient air supply based on the difference in flow field between elbows and straight pipes.

[0015] Furthermore, the game-theoretic decision-making module acquires system operating status information and execution status data once per second, with no fewer than 20 sampling points for each acquisition; the multi-objective game cost algorithm calculates pipeline blockage risk value, transmission energy consumption value, and ash-to-gas ratio matching degree once per second, calling the latest acquired operating status data during the calculation process; in the generated control commands, the regulating valve opening adjustment range is 0-100%, the gas replenishment valve action interval range is 0.1-10 seconds, the single gas replenishment duration range is 0.1-5 seconds, the control commands are transmitted through a specified protocol, the transmission frequency is consistent with the data acquisition frequency, and the commands are stored in a dedicated cache unit.

[0016] Compared with existing technologies, this intelligent anti-clogging and energy-saving full-pipe conveying system for pneumatic ash conveying pipelines has the following advantages: I. This invention deploys sensing units along key areas of the ash conveying pipeline and links them with multiple monitoring devices. Through anti-interference processing and a global operating condition fusion algorithm, it integrates multi-dimensional operational data. A distributed transmission network is built using dedicated bus power supply and prefabricated quick connectors. Full-duplex communication enables synchronous interaction between data uplink and command downlink. A multi-objective game cost algorithm balances the risk of pipe blockage, conveying energy consumption, and the ash-to-gas ratio. Combined with a gradient fluidization optimal output algorithm, it generates precise control commands. This allows for real-time identification of pipeline blockage risks and dynamic optimization of conveying gas volume and replenishment strategies, ensuring the ash conveying process maintains a stable, full-pipe operation. This effectively reduces pipeline blockage failures, minimizes gas waste and equipment wear, and improves the continuity of the ash conveying system and material conveying efficiency. The overall conveying process better matches actual operating conditions, achieving precise adaptation between operating status and control commands.

[0017] II. This invention employs differentiated air supply control methods for different areas of the ash conveying pipeline, implementing fixed-point air supply and gradient air supply for horizontal bends and straight pipe sections respectively. It utilizes intelligent regulating valves to flexibly adjust the total conveyed air volume and transmit execution status in real time, forming a complete closed-loop control mechanism. Coupled with an independently set safety protection module that monitors pressure data in real time and implements anti-backflow protection, and using an external structure decoupled from the original system to ensure operational independence, it simultaneously monitors power supply and communication status and quickly switches operating modes. This not only enhances the stability of the pipeline flow field, avoiding localized ash accumulation and pipeline wear, but also improves system operational safety from both hardware and logic perspectives, reducing operational risks caused by emergencies such as backflow, power supply anomalies, and communication interruptions. This ensures long-term stable and reliable operation of the ash conveying system, extends the service life of pipelines and actuators, and reduces system maintenance difficulty and costs, thus comprehensively improving the overall operational quality of pneumatic ash conveying.

[0018] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0020] Figure 1 Flowchart of an intelligent anti-clogging and energy-saving full-pipe conveying system for pneumatic ash conveying pipelines; Figure 2 Framework diagram of intelligent anti-clogging and energy-saving full-pipe conveying system for pneumatic ash conveying pipeline; Figure 3 This is a data transmission diagram within the game decision-making module. Detailed Implementation

[0021] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0022] Reference Figure 1 One embodiment of the present invention proposes an intelligent anti-clogging and energy-saving full-pipe conveying system for pneumatic ash conveying pipelines. It adopts a collaborative operation mechanism of full-domain operating condition data fusion acquisition, distributed bus bidirectional transmission, multi-objective game intelligent decision-making, zoned gradient fluidization adjustment, and independent decoupled safety protection. In the pneumatic conveying scenario of fly ash in thermal power industry, it can perceive pipeline operating parameters in real time throughout the process, dynamically match the conveying gas volume and zoned gas replenishment strategy, and stably achieve full-pipe conveying state. At the same time, it reduces the risk of pipeline blockage, reduces compressed air energy consumption and wear of valves and pipeline equipment, and improves the continuous operation stability and safety protection capability of pneumatic ash conveying system.

[0023] The system described in this embodiment specifically utilizes five functional modules—operating condition acquisition, bus transmission, game theory decision-making, fluidization adjustment, and safety assurance—to work collaboratively, forming a complete closed-loop intelligent control system. After the system completes power-on initialization and equipment self-test, each module starts and runs synchronously according to a fixed sequence, sequentially completing the entire closed-loop operation of data acquisition, data transmission, intelligent decision-making, execution adjustment, and safety protection. Specific implementation procedures and operational details are as follows: Figure 2 As shown: The operating condition acquisition module performs multi-dimensional data acquisition and full-domain operating condition fusion calculation: The operating status acquisition module is the basic unit for the system to acquire operating status information. After the system starts, this module first completes the on-site fixing and wiring connection of all sensing devices and detection devices according to the preset standardized layout specifications. The layout positions of all devices cover the key points of the entire ash conveying pipeline that are prone to blockage, pressure fluctuation, and material confluence. The specific layout rules are as follows: in the straight pipe section of the economizer ash conveying pipeline, pressure sensing units are laid out at fixed intervals; in the straight pipe section of the electric field ash conveying pipeline, pressure sensing units are laid out at another fixed interval; pressure sensing units are symmetrically laid out at the front and rear ends of all horizontal bends in the ash conveying pipeline; a pressure sensing unit is laid out separately at a designated position behind the discharge valve of the silo pump; and pressure sensing units are laid out in pairs at the junction of the ash conveying branch pipe and the ash conveying main pipe. The ash content detection device is fixedly connected to the inlet of the ash conveying pipeline to ensure real-time collection of fly ash content data entering the pipeline; the load monitoring unit is connected to the unit's DCS system through a communication interface to stably read the unit's real-time power generation load data; the flow meter is sealed and installed on the main gas conveying pipeline near the outlet of the silo pump to accurately collect the instantaneous flow data of the conveyed gas; the air compressor parameter terminal is connected to the air compressor control cabinet through a control interface to collect the air compressor's operating output data in real time.

[0024] After all equipment is deployed and connected, the operating condition acquisition module enters a stable data acquisition state, synchronously acquiring six types of core operating data at a fixed high frequency: pressure data at various points in the ash conveying pipeline, pressure data of the conveying gas source, ash content data of the coal entering the furnace, real-time power generation load data of the unit, instantaneous flow rate data of the conveying gas, and real-time output data of the air compressor. Simultaneously, based on the acquired conveying gas flow rate data and coal ash content data, the ash-to-gas ratio parameter is calculated in real time. After the raw data acquisition is completed, the module immediately performs preprocessing operations on all acquired data. First, it uses hardware anti-interference circuitry and software filtering algorithms to filter out noise interference caused by electromagnetic interference, equipment mechanical vibration, and signal transmission attenuation in the industrial environment. Then, it uses a multi-point calibration algorithm to correct measurement errors caused by sensor zero-point drift and temperature drift, ensuring that all data entering the calculation stage has high accuracy and high stability.

[0025] After data preprocessing, the operating condition acquisition module calls the global operating condition fusion algorithm to perform weighted integration calculations on the multi-dimensional preprocessed data, generating a system-wide fusion state value that can comprehensively quantify the overall operating status of the ash conveying pipeline. The complete mathematical expression of this algorithm is:

[0026] in, This represents the system's overall fusion status value. For the weighting coefficient of pipeline pressure distribution, Weighted coefficient of coal quality entering the furnace, For unit load weighting factor, The weighting coefficients are for the gas flow rate, and all weighting coefficients are dimensionless coefficients ranging from 0 to 1, and satisfy the following conditions: The parameter is the arithmetic mean of the pressure data collected from all points along the ash conveying pipeline, after being normalized using the Min-Max method. The parameter is the real-time ash content value output by the coal ash content detection device after Min-Max normalization. The parameters are the real-time power generation load values ​​output by the unit load monitoring unit after Min-Max normalization. The parameter is the real-time instantaneous flow rate value output by the gas flow meter after Min-Max normalization. The weighting coefficients in the global operating condition fusion algorithm are calculated using the normalized weight allocation method, and the specific mathematical expression is as follows: ; ; ; ;in This is the weighting coefficient for pipeline pressure distribution. This is the weighted coefficient for the quality of coal entering the furnace. This is the unit load weighting factor. The weighting factor for the gas flow rate is... The baseline value for the weighting of the impact of pipeline pressure distribution. The weighting benchmark value for the influence of ash content on the quality of coal entering the furnace. The baseline value for the weighting of unit load impact. To ensure that the weighting benchmark values ​​affect the gas flow rate, all four weighting benchmark values ​​are non-negative real numbers calibrated based on the actual working conditions of pneumatic ash conveying at thermal power plants, and the weighting coefficients of the four categories after calculation satisfy the constraint that the sum of their values ​​is 1. The normalization process is calculated using the Min-Max normalization formula, specifically as follows: ,in These are the normalized parameters. These are the original collected parameters. This is the minimum value collected historically for this parameter. This is the historical maximum value of the parameter. This formula can be used to uniformly map all parameters to the range of 0 to 1.

[0027] After the algorithm calculation is completed, the operating condition acquisition module integrates the original acquired data, the ash-to-gas ratio calculation results, and the system's overall fusion status values ​​to generate a complete system operating condition status information dataset. This dataset is then uploaded to the game decision-making module in real time via the bus transmission module, providing comprehensive, accurate, and real-time data support for subsequent intelligent decision-making processes.

[0028] For example, in a scenario where a thermal power plant generator unit is operating normally and the pneumatic conveying of fly ash is continuously carried out, the operating condition acquisition module completes the installation of all sensing devices according to a fixed deployment rule, and continuously and synchronously collects real-time data on pipeline pressure, gas source pressure, coal ash content, unit load, conveyed gas flow rate, and air compressor output. After performing anti-interference filtering and zero-point calibration on the collected data, the module calls the full-domain operating condition fusion algorithm to complete weighted calculations, obtaining a system full-domain fusion state value that can intuitively reflect the overall operating status of the pipeline. Then, all data are integrated into a standardized dataset and stably uploaded to the game decision module through the bus transmission module. The entire process of data acquisition is uninterrupted, data processing is error-free, and data transmission is delay-free, providing a complete state basis for the subsequent intelligent control of the system.

[0029] The bus transmission module constructs a distributed network and enables bidirectional full-duplex communication: The bus transmission module is the core channel for system data interaction and command transmission. During system operation, this module adopts a combination of centralized power supply via DC48V bus and quick access via prefabricated quick connectors to build a distributed communication network covering all monitoring points and execution terminals along the entire ash conveying pipeline. The network access nodes include all pressure sensing units, ash detection devices, load monitoring units, flow meters, air compressor parameter terminals, intelligent regulating valves, and zone air supply valves. All nodes are connected via prefabricated quick connectors for plug-and-play operation, eliminating the need for additional dedicated communication cables. This effectively reduces on-site construction difficulty and system deployment costs while ensuring the scalability and maintenance convenience of the network nodes.

[0030] After the distributed network is built, the bus transmission module adopts a dedicated full-duplex communication protocol to divide the data transmission channel into an independently operating uplink transmission channel and a downlink transmission channel. The two channels work synchronously and do not interfere with each other: the uplink transmission channel is dedicated to data uploading, transmitting the system operating status information dataset output by the operating condition acquisition module to the game decision module in real time without congestion or packet loss; the downlink transmission channel is dedicated to command issuance, transmitting the regulating valve opening control command and the air replenishment valve action timing control command generated by the game decision module to each execution terminal of the fluidization control module accurately and quickly, realizing synchronous bidirectional interaction between data uploading and command issuance, and completely avoiding problems such as data transmission delay, command loss, and signal congestion.

[0031] The communication frequency of the bus transmission module is completely consistent with the data acquisition frequency of the operating condition acquisition module, completing a full data upload and command issuance cycle every second. The number of operating condition data sampling points transmitted each time meets the system's preset requirements, ensuring the integrity and real-time performance of the transmitted information and perfectly matching the timing requirements of the dynamic control of the pneumatic ash conveying system. Simultaneously, this distributed network has no hardware connection with the existing pneumatic ash conveying control system on-site. All communication data uses an independent encoding format, ensuring no interference with the original system's operating logic. The network features automatic reconnection after disconnection, automatic signal verification, and automatic fault alarm functions, guaranteeing the stability and reliability of the communication link throughout the entire process.

[0032] The game theory decision-making module completes multi-objective cost calculation and optimal control command generation: The game theory decision-making module is the intelligent control unit of the system. During system operation, this module synchronously acquires two types of key data at a fixed frequency through the bus transmission module. The first type of data is the system operating condition status information uploaded by the operating condition acquisition module, including the original data collected from each monitoring point, the ash-to-gas ratio calculation parameters, and the fused status value of the overall operating condition. The second type of data is the execution status data returned by the fluidization regulation module, including the real-time opening degree of the intelligent regulating valve, the on / off status of the zoned gas supply valve, and the flow field status data inside the pipeline. All acquired data is the latest real-time data, with no historical lag data. Figure 3 As shown.

[0033] After acquiring real-time data, the game decision-making module first calls the multi-objective game cost algorithm to calculate the pipeline blockage risk value, the transmission energy consumption value, and the ash-to-gas ratio matching degree based on the latest operating data. Finally, it integrates these values ​​to obtain the total game cost value of the system. The complete mathematical expression of this algorithm is as follows:

[0034] in, This represents the total game cost of the system. To prevent congestion, an adaptive weighting coefficient is used. This is a real-time value representing the risk and cost of pipe blockage calculated based on pipeline pressure distribution. An adaptive weighting coefficient for energy-saving costs. This is a real-time energy consumption cost calculated based on the air flow rate and air compressor output. An adaptive weighting coefficient for equipment wear cost. This is a real-time equipment wear cost value calculated based on the frequency of air replenishment valve operation and the change in the opening degree of the regulating valve; The adaptive weight coefficients in the multi-objective game cost algorithm are calculated using a dynamic priority allocation method, and the specific mathematical expression is as follows: ; ; ;in To prevent congestion, an adaptive weighting coefficient is used. An adaptive weighting coefficient for energy-saving costs. An adaptive weighting coefficient for equipment wear cost. The benchmark value for the risk cost of pipe blockage is the weighting value. As the benchmark value for energy consumption cost weighting, The three types of weight benchmark values ​​are non-negative real numbers calibrated according to the system's anti-blocking, energy-saving, and loss-reduction operation priorities, and the calculated three types of adaptive weight coefficients satisfy the constraint that the sum of their values ​​is 1. Real-time pipe blockage risk and cost values The calculation is performed using the cumulative formula for the squared deviation of pipeline pressure, i.e. ;in For the first Real-time pressure values ​​at each pressure acquisition point Pressure setpoint for safe pipeline operation; real-time energy consumption cost. The calculation is performed using the integral formula of the product of flow rate and output, i.e. ;in To deliver gas flow rate in real time, To provide real-time output for the air compressor, For calculating the time period; real-time equipment wear cost values. The calculation is performed using the formula that multiplies the frequency of actions by the change in opening, i.e. ;in The frequency of operation of the air replenishment valve per unit time. This represents the change in valve opening per unit time.

[0035] The algorithm's calculation cycle is consistent with the data acquisition frequency, completing a full calculation every second. Each calculation uses the latest collected operating data to ensure that the cost calculation results are completely matched with the real-time operating status of the pipeline, thus completely avoiding control deviations caused by delayed calculations.

[0036] After calculating the total system game cost, the game decision module immediately invokes the gradient fluidization optimal output algorithm. Combining the system's global fusion state value, the total system game cost, and the flow field difference parameters in different regions of the pipeline, it generates the optimal action output value. The complete mathematical expression of this algorithm is:

[0037] in, Output values ​​for the optimal action to be performed. The system state coupling coefficient is... The system global fusion state value output by the global operating condition fusion algorithm. The cost index is the suppression coefficient. This represents the total system game cost value output by the multi-objective game cost algorithm. This is the pipeline gradient fluidization correction factor. The pipeline gradient gas injection correction value is set based on the difference in flow field between elbows and straight pipes; The coefficients in the gradient fluidization optimal output algorithm are calculated using the flow field matching assignment method, and the specific mathematical expression is as follows: ; ; ;in The system state coupling coefficient is... The cost index is the suppression coefficient. This is the pipeline gradient fluidization correction factor. The system state affects the baseline weight value. The cost indicator affects the weighted benchmark value. The three types of weighted benchmark values ​​are non-negative real numbers calibrated based on the flow field characteristics of the horizontal bends and straight sections of the ash conveying pipeline, and the three types of coefficients satisfy the constraint that the sum of their values ​​is 1 after calculation. Pipeline gradient gas supply correction value The calculation is performed using the formula for the flow field difference between elbows and straight pipes, i.e. ;in The real-time flow velocity in the horizontal bend region. This represents the real-time flow field velocity in the straight pipe section.

[0038] Based on the optimal execution action output value, the game theory decision-making module generates two types of precise control commands. The first type is an intelligent regulating valve opening control command, which includes the continuous opening setting value of the regulating valve, covering the entire range of opening adjustment and enabling continuous stepless adjustment. The second type is a zoned air supply valve action sequence control command, which explicitly includes the start time, action interval, single continuous action duration, and action start / stop sequence of the air supply valves in horizontal bend areas and straight pipe sections. After all control commands are generated, they are stored in a dedicated cache unit within the module and then sent to the fluidization control module in real time via the bus transmission module to avoid execution anomalies caused by command transmission interruptions.

[0039] For example, during the continuous and stable operation of the pneumatic ash conveying system, the game theory decision-making module acquires real-time operating condition data and fluidization regulation execution feedback data through the bus transmission module. It calls the multi-objective game cost algorithm to integrate and calculate the costs of three types: pipe blockage risk, conveying energy consumption, and equipment wear. After obtaining the total game cost value of the system, it calculates the optimal execution action through the gradient fluidization optimal output algorithm. Based on the calculation results, it generates regulating valve opening instructions and zone air replenishment valve timing instructions that are adapted to the current pipeline operating state. After the instructions are generated, they are immediately sent to the fluidization regulation module through the bus transmission module. The entire decision-making calculation is without delay, the instruction generation is without deviation, and the instruction sending is without lag, ensuring that the control strategy is highly matched with the real-time pipeline state.

[0040] The fluidization control module performs zone-specific gas replenishment and total gas delivery volume adjustment: The fluidization control module is the system's execution terminal unit. During system operation, this module receives control commands from the game decision-making module in real time via the bus transmission module. Based on the flow field characteristics, material conveying patterns, and blockage risk levels of different areas of the ash conveying pipeline, it performs differentiated air replenishment operations and precise adjustment of the total conveying air volume. The specific execution process is divided into three stages, each operating synchronously and in coordination: The first stage is the fixed-point air replenishment operation in the horizontal bend area. Horizontal bends are high-risk areas where fly ash is easily deposited and the flow field is easily disturbed. The module performs high-frequency fixed-point air replenishment in this area, completing the air replenishment action at a fixed short interval frequency, and controlling the air replenishment volume within a preset reasonable range. By precisely replenishing the air at the bend, the material accumulation at the bend is broken, the flow field in the bend area is stabilized, and the pipe blockage is eliminated from the source. The first step involves a gradient gas replenishment operation in the straight pipe section. In this section, the flow field is stable and material transport is smooth. The module performs low-frequency gradient gas replenishment in this area, completing the replenishment action at fixed long intervals. The replenishment gas volume is controlled within a preset reasonable range, and the replenishment gas volume decreases gradually along the material transport direction of the pipeline, perfectly matching the flow field law of the material transport in the straight pipe section and avoiding excessive replenishment that would waste compressed air energy. The second step is the total transport gas volume adjustment operation. The module adjusts the total transport gas volume in real time through an intelligent regulating valve. The regulating valve adjusts its opening at a fixed frequency, covering the entire range, ensuring that the total transport gas volume is perfectly matched with the current coal ash content, unit power generation load, and pipeline pressure status, stably achieving full-pipe transport without excessive or insufficient gas supply.

[0041] While the fluidization control module performs the control operation, it integrates the real-time control status of the intelligent control valve, the operating status of the horizontal elbow air supply valve, the operating status of the straight pipe section air supply valve, and relevant data of the internal flow field of the pipeline at a fixed frequency to generate standardized execution status data. This data is then transmitted back to the game decision module in real time via the bus transmission module, forming a complete closed-loop control system of data acquisition, intelligent decision-making, command execution, and status feedback, ensuring the continuity, accuracy, and stability of the system control.

[0042] For example, during the continuous transport of fly ash in the pneumatic ash conveying pipeline, after receiving the control command issued by the game decision module, the fluidization control module immediately performs high-frequency fixed-point air replenishment in the high-risk area of ​​the horizontal bend to continuously break up material deposits. At the same time, it performs gradient-decreasing air replenishment in the straight pipe section to match the flow field law of the straight pipe. Then, the total transported air volume is adjusted in real time through the intelligent regulating valve to maintain the full pipe transport state. During the execution process, the operating status of all equipment and flow field data are simultaneously transmitted back to the game decision module, allowing the decision module to grasp the execution effect in real time, continuously optimize the control command, and operate in a closed loop without disconnection or deviation.

[0043] The security module enables independent security protection and intelligent switching of operating modes: The safety protection module is an independent protection unit of the system. This module adopts an external structure that is completely decoupled from the existing pneumatic ash conveying control system on site. It has no hardware dependence or control logic association with the original system. It adopts an independent power supply mode, and the power supply voltage range meets the system's preset requirements. It does not rely on the original system's power supply network, thus completely avoiding safety protection failures caused by power supply failures or control failures in the original system.

[0044] During system operation, the safety protection module compares pipeline pressure data with gas source pressure data in real time at a high frequency, keeping the absolute value of the pressure difference between the pipeline and gas source pressure within a safe range. When the absolute value of the pressure difference exceeds the preset safety threshold, the module immediately activates the anti-backflow linkage protection action. By quickly adjusting the gas source and pipeline pressure, it achieves rapid pressure balance, effectively preventing safety issues such as material backflow, pipeline pressure buildup, and damage to conveying equipment.

[0045] Meanwhile, the safety protection module continuously monitors the independent power supply voltage and distributed network communication status at a fixed frequency. When the independent power supply voltage is detected to be lower than the preset safe voltage value, or the distributed network communication interruption lasts for more than the preset duration, the module immediately triggers the operation mode switching operation. The mode switching response time meets the preset requirements of the system. After the switching is completed, the basic ash conveying control logic is retained to ensure the normal operation of the basic conveying function of the pneumatic ash conveying system and avoid operation interruption. When the independent power supply voltage returns to the safe range and the distributed network communication is reconnected and stabilized, the module automatically switches back to the intelligent control mode within the preset time, restores the full-function closed-loop operation, and provides full protection without blind spots and switching without delay.

[0046] For example, during the operation of the pneumatic ash conveying system, the safety protection module continuously monitors the pipeline pressure and the air source pressure at high frequency. When the pressure difference exceeds the safety threshold, it immediately activates the anti-ash-fall protection action to quickly balance the pressure and eliminate safety hazards. At the same time, it continuously monitors the power supply and communication status. When the power supply voltage is abnormal, it quickly switches to the basic operation mode to ensure the continuous operation of ash conveying. After the power supply is restored to normal, it automatically switches back to the intelligent control mode. It operates and is protected independently throughout the entire process, without relying on the original system, effectively ensuring the operational safety and continuity of the pneumatic ash conveying system.

[0047] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A pneumatic ash conveying pipeline intelligent anti-clogging and energy-saving full-pipe conveying system, characterized in that, The system includes: Operating condition acquisition module: Pressure sensing units are deployed along the ash conveying pipeline and communicate with the ash content detection device, load monitoring unit, flow meter, and air compressor parameter terminal to collect pipeline pressure, air source pressure, coal ash content, unit load, conveying air flow rate, and air compressor output data, and calculate the ash-to-air ratio. After anti-interference and calibration processing, the data are integrated into a system operating condition status information dataset through a full-domain operating condition fusion algorithm. Bus transmission module: It adopts DC48V bus power supply and prefabricated quick connectors to build a distributed network. The uplink transmits the system operating status information dataset output by the operating condition acquisition module, and the downlink transmits the control commands output by the game decision module to the execution terminal. The interaction is realized by using a full-duplex communication protocol. Game Theory Decision Module: It obtains system operating status information and execution status data returned by fluidization regulation module through bus transmission module, calculates pipeline blockage risk value, transport energy consumption value and ash gas ratio matching degree using multi-objective game cost algorithm, and generates regulating valve opening command and gas replenishment valve action timing command through gradient fluidization optimal output algorithm, and sends the control command to bus transmission module. Fluidization control module: It obtains control commands from the bus transmission module, performs fixed-point air replenishment in the horizontal bend area, performs gradient air replenishment in the straight pipe section area, adjusts the total air delivery volume through the intelligent regulating valve, and transmits the regulating valve opening, air replenishment valve status, and flow field status data back to the bus transmission module as execution status data. Safety assurance module: Real-time comparison of pipeline pressure and gas source pressure data, implementation of anti-ash spillage linkage protection, adopts an external structure decoupled from the original system, monitors power supply and communication status and switches operating modes.

2. The intelligent anti-clogging and energy-saving full-pipe conveying system for pneumatic ash conveying pipelines according to claim 1, characterized in that, The pressure sensing units of the operating condition acquisition module are arranged one every 3 meters along the straight section of the economizer ash conveying pipeline, one every 5 meters along the straight section of the electric field ash conveying pipeline, one 0.5 meters before and after the horizontal bend, one 1 meter after the discharge valve of the silo pump, and two at the junction of the ash conveying branch pipe and the main pipe; the ash content detection device is connected to the inlet of the ash conveying pipeline, the load monitoring unit is connected to the unit's DCS system, the flow meter is installed on the main gas conveying pipeline near the outlet of the silo pump, and the air compressor parameter terminal is connected to the air compressor control cabinet.

3. The intelligent anti-clogging and energy-saving full-pipe conveying system for pneumatic ash conveying pipelines according to claim 1, characterized in that, In the aforementioned operating condition acquisition module, the mathematical expression for the full-domain operating condition fusion algorithm is: ； in, This represents the system's overall fusion status value. For the weighting coefficient of pipeline pressure distribution, Weighted coefficient of coal quality entering the furnace, For unit load weighting factor, The weighting coefficients are for the gas flow rate, and all weighting coefficients are dimensionless coefficients ranging from 0 to 1, and satisfy the following conditions: The parameter is the arithmetic mean of the pressure data collected from all points along the ash conveying pipeline, after being normalized using the Min-Max method. The parameter is the real-time ash content value output by the coal ash content detection device after Min-Max normalization. The parameters are the real-time power generation load values ​​output by the unit load monitoring unit after Min-Max normalization. The parameter is the real-time instantaneous flow rate value output by the gas flow meter after Min-Max normalization.

4. The intelligent anti-clogging and energy-saving full-pipe conveying system for pneumatic ash conveying pipelines according to claim 1, characterized in that, The distributed network of the bus transmission module covers all monitoring points and execution terminals of the ash conveying pipeline; the full-duplex communication protocol undertakes data uplink and command downlink tasks respectively, realizing synchronous bidirectional full-duplex interaction.

5. The intelligent anti-clogging and energy-saving full-pipe conveying system for pneumatic ash conveying pipelines according to claim 1, characterized in that, In the game decision-making module, the execution status data includes the real-time opening degree of the regulating valve, the on / off status of the air supply valve, and the pipeline flow field status data. The system operating status information includes the original data collected at each monitoring point and the data processed by the global operating status fusion algorithm. The regulating valve opening command includes the opening setting value of each regulating valve in the ash conveying pipeline. The generated air supply valve action timing command includes the start time, action interval duration, continuous action duration, and action start / stop sequence of the air supply valve in the horizontal bend area and the straight pipe section area.

6. The intelligent anti-clogging and energy-saving full-pipe conveying system for pneumatic ash conveying pipelines according to claim 1, characterized in that, The mathematical expression for the multi-objective game cost algorithm of the game decision module is as follows: ； in, This represents the total game cost of the system. To prevent congestion, an adaptive weighting coefficient is used. This is a real-time value representing the risk and cost of pipe blockage calculated based on pipeline pressure distribution. An adaptive weighting coefficient for energy-saving costs. This is a real-time energy consumption cost calculated based on the air flow rate and air compressor output. An adaptive weighting coefficient for equipment wear cost. This is a real-time equipment wear cost value calculated based on the frequency of air replenishment valve operation and the change in the opening degree of the regulating valve.

7. The intelligent anti-clogging and energy-saving full-pipe conveying system for pneumatic ash conveying pipelines according to claim 1, characterized in that, The mathematical expression for the gradient fluidized optimal output algorithm of the game decision module is: ； in, Output values ​​for the optimal action to be performed. The system state coupling coefficient is... The system global fusion state value output by the global operating condition fusion algorithm. The cost index is the suppression coefficient. This represents the total system game cost value output by the multi-objective game cost algorithm. This is the pipeline gradient fluidization correction factor. This is the correction value for pipeline gradient air supply based on the difference in flow field between elbows and straight pipes.

8. The intelligent anti-clogging and energy-saving full-pipe conveying system for pneumatic ash conveying pipelines according to claim 1, characterized in that, The game decision-making module acquires system operating status information and execution status data once per second, with no less than 20 sampling points for each acquisition. The multi-objective game cost algorithm calculates pipeline blockage risk value, transmission energy consumption value, and ash-to-gas ratio matching degree once per second. The calculation process calls the latest acquired operating status data. In the generated control commands, the regulating valve opening adjustment range is 0-100%, the gas replenishment valve action interval range is 0.1-10 seconds, and the single gas replenishment duration range is 0.1-5 seconds.

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