Intelligent anti-blocking, abrasion-reducing and energy-saving ash conveying system

Abstract

The invention discloses an intelligent anti-blocking, wear-reducing and energy-saving ash conveying system, which comprises a sensing module, which comprises at least one pressure sensing array and at least one acoustic emission sensing array arranged at different positions of an ash conveying pipeline and is used for collecting dynamic pressure signals and acoustic emission signals of material conveying in the pipeline in real time; and the control module is in communication connection with the sensing module. According to the intelligent anti-blocking, abrasion-reducing and energy-saving ash conveying system disclosed by the embodiment of the invention, accurate sensing of the operation state of the ash conveying pipeline and early warning of the pipe blocking risk are realized through multi-sensor information fusion and an intelligent algorithm. And an adopted grading intervention strategy can accurately adjust operation parameters according to risk grades, so that excessive intervention is effectively avoided while the conveying reliability is guaranteed, and compressed air consumption and equipment abrasion are remarkably reduced.

Description

Technical Field

[0001] This application relates to the field of conveying technology, and in particular to an intelligent anti-clogging, wear-reducing, and energy-saving ash conveying system. Background Technology

[0002] Pneumatic ash conveying systems are widely used in power, chemical and other fields. Their core function is to transport dry bulk materials such as fly ash to storage points through pipelines. Currently, most existing ash conveying systems adopt a positive pressure pneumatic conveying method with a silo pump as the core, and generally operate based on timed or simple constant pressure control logic. Specifically, the system completes the processes of "feeding, fluidization, conveying, and purging" in a preset time sequence, or only starts and stops the conveying process when the pressure inside the pump reaches a certain fixed threshold.

[0003] The problem with this control method lies in its lack of perception of the actual transport status within the pipeline. The system cannot effectively identify initial signs of blockage such as ash plug formation and material adhesion, and can only respond after the pipeline pressure has significantly increased or even completely blocked. To conservatively address the risk of pipe blockage, existing systems typically adopt a "high air velocity, low concentration" operating strategy, barely maintaining smooth transport by continuously maintaining a high airflow velocity. This mode not only leads to high compressed air energy consumption but also exacerbates the erosion and wear of particulate materials on the pipe walls (especially at bends). Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, one objective of this application is to provide an intelligent anti-clogging, wear-reducing, and energy-saving ash conveying system. Through multi-sensor information fusion and intelligent algorithms, it achieves precise perception of the operating status of the ash conveying pipeline and early warning of blockage risks. The adopted graded intervention strategy can accurately adjust operating parameters according to the risk level, effectively avoiding excessive intervention while ensuring conveying reliability, significantly reducing compressed air consumption and equipment wear. The predictive maintenance function provided by the system can identify pipe section wear trends in advance and optimize maintenance cycles, thereby improving overall system operating efficiency, extending equipment lifespan, and achieving comprehensive benefits of anti-clogging, wear reduction, and energy saving.

[0006] To achieve the above objectives, the first aspect of this application proposes an intelligent anti-clogging, wear-reducing, energy-saving ash conveying system, comprising: The sensing module includes at least one pressure sensing array and at least one acoustic emission sensing array set at different positions in the ash conveying pipeline, for real-time acquisition of dynamic pressure signals and acoustic emission signals of material conveying in the pipeline; A control module, communicatively connected to the sensing module, includes: The risk prediction unit is configured to calculate a comprehensive pipe blockage risk index based on the dynamic pressure signal and the acoustic emission signal. An adaptive intervention unit is configured to automatically adjust the system's operating parameters when the blockage risk index exceeds a first preset threshold. The execution module is communicatively connected to the control module and is used to execute the parameter adjustment commands issued by the adaptive intervention unit.

[0007] The intelligent anti-clogging, wear-reducing, and energy-saving ash conveying system according to embodiments of this application achieves precise perception of the operating status of the ash conveying pipeline and early warning of pipe blockage risks through multi-sensor information fusion and intelligent algorithms. The adopted graded intervention strategy can accurately adjust operating parameters according to the risk level, effectively avoiding excessive intervention while ensuring conveying reliability, significantly reducing compressed air consumption and equipment wear. The predictive maintenance function provided by the system can identify pipe section wear trends in advance and optimize maintenance cycles, thereby improving overall system operating efficiency, extending equipment lifespan, and achieving comprehensive benefits of anti-clogging, wear reduction, and energy saving.

[0008] In addition, the intelligent anti-clogging, grinding-reducing, energy-saving ash conveying system proposed in this application may also have the following additional technical features: Specifically, the pressure sensing array includes at least three high-frequency dynamic pressure sensors distributed at equal or non-equal intervals along the ash conveying pipeline, and the data sampling frequency of the pressure sensors is not less than 100Hz.

[0009] Specifically, the acoustic emission sensing array includes at least three acoustic emission sensors paired with the pressure sensor, the acoustic emission sensors having a resonant frequency range of 30kHz to 80kHz, and being attached to the outer wall of the pipe via a magnetic base.

[0010] Specifically, when calculating the blockage risk index, the risk prediction unit considers at least the following factors: The relative pressure gradient factor is calculated based on the pressure sensor array data. The pressure gradient deterioration rate factor is calculated based on the relative pressure gradient change rate. Local flow anomaly factor calculated based on the pressure sensor array data; The acoustic anomaly factor is calculated based on the acoustic emission sensor array data.

[0011] Specifically, the adaptive intervention unit is configured to execute a tiered intervention strategy: When the blockage risk index exceeds the first threshold but does not exceed the second threshold, an early warning signal is issued and system status data is recorded; When the pipe blockage risk index exceeds the second threshold but does not exceed the third threshold, an instruction is sent to the execution module to execute a primary intervention strategy including a pulse oscillation program and / or a reduction in the feed rate. When the blockage risk index exceeds the third threshold, an instruction is sent to the execution module to execute an emergency intervention strategy, including increasing the system delivery pressure setpoint and forcing operation until the risk is eliminated.

[0012] Specifically, the pulse oscillation procedure includes: increasing the opening of the intake valve by 10%-30% from its original position and maintaining it for 2-5 seconds, then returning it to its original opening position.

[0013] Specifically, the control module adopts a hybrid architecture consisting of a programmable logic controller and an industrial computer, wherein the risk prediction unit and the adaptive intervention unit run on the industrial computer.

[0014] Specifically, the control module is further configured as follows: Based on long-term trend analysis of the acoustic emission sensor data, the cumulative wear of pipe bends or specific pipe sections is calculated. When the cumulative wear exceeds a preset threshold, a maintenance reminder is generated, which includes a suggested replacement location for the pipe section or an estimated remaining service life.

[0015] Specifically, the control module is configured to perform wear monitoring and early warning according to the following steps: Learning period: In the initial stage of system operation, the root mean square value of acoustic emission signal of each pipe section is collected within at least 100 ash conveying cycles, and the average value of these root mean square values ​​of acoustic emission signal is calculated as the reference acoustic energy level of the pipe section. Monitoring and accumulation: In subsequent operation, the increment of the root mean square value of the acoustic emission signal relative to the reference level in each ash conveying cycle is calculated in real time, and this increment is included in the cumulative wear equivalent of the corresponding pipe section; Dynamic early warning: When the cumulative wear equivalent of a certain pipe section exceeds the first dynamic threshold, an early maintenance reminder is generated; when the cumulative wear equivalent exceeds the higher second dynamic threshold, an emergency replacement reminder is generated. The first and second dynamic thresholds are dynamically adjusted based on the historical wear rate of the pipe section and the preset safe operation cycle. The remaining service life estimate is calculated by subtracting the current cumulative wear equivalent from the maximum permissible cumulative wear equivalent, and then dividing by the recent average wear rate.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a system block diagram of an intelligent anti-clogging, wear-reducing, energy-saving ash conveying system according to an embodiment of this application. Detailed Implementation

[0018] Embodiments of this application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. Rather, embodiments of this application include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0019] The following is in conjunction with the appendix Figure 1 This application describes an intelligent anti-clogging, wear-reducing, energy-saving ash conveying system, comprising: The sensing module includes at least one pressure sensing array and at least one acoustic emission sensing array set at different positions in the ash conveying pipeline, for real-time acquisition of dynamic pressure signals and acoustic emission signals of material conveying in the pipeline; The control module, which communicates with the sensing module, includes: The risk prediction unit is configured to calculate a comprehensive pipe blockage risk index based on dynamic pressure signals and acoustic emission signals. The adaptive intervention unit is configured to automatically adjust the system's operating parameters when the pipe blockage risk index exceeds a first preset threshold.

[0020] The execution module, which communicates with the control module, is used to execute parameter adjustment commands issued by the adaptive intervention unit.

[0021] Specifically, the ash conveying system mainly includes three functional modules: (1) The sensing module consists of a pressure sensing array and an acoustic emission sensing array set at key locations in the ash conveying pipeline, and is responsible for collecting dynamic pressure signals and acoustic emission signals during the conveying process; (2) Control module, which includes a risk prediction unit and an adaptive intervention unit, is responsible for signal processing, risk analysis and decision control; (3) Execution module, including air intake regulating valve, feed control device, etc., is responsible for executing control commands.

[0022] The pressure sensor array consists of three equidistantly distributed high-frequency pressure sensors, installed 5 meters from the pump outlet, in the middle of the pipeline, and 5 meters from the end of the pipeline, respectively. The acoustic emission sensor array uses acoustic emission sensors with a resonant frequency of 30-80kHz, which are paired with each pressure sensor and installed with additional monitoring points behind the pipeline bends.

[0023] During system operation, the risk prediction unit continuously receives real-time data from the sensing module. By analyzing the pressure gradient change trend and acoustic emission signal characteristics, it calculates the pipe blockage risk index PRI. The calculation of this index comprehensively considers the following four factors: relative pressure gradient, pressure gradient change rate, local flow anomaly index, and acoustic anomaly index.

[0024] When the PRI value exceeds the preset threshold, the adaptive intervention unit activates the corresponding control strategy: When the PRI value is in the range of 30-60, the system issues a warning signal and records the operating data; When the PRI value is in the range of 60-85, the system performs a primary intervention, which includes applying a 3-second boost pulse and reducing the feed valve opening by 10%. When the PRI value exceeds 85, the system immediately switches to the powerful cleaning mode, increasing the delivery pressure setting by 25% until the risk index falls back to a safe range.

[0025] In one embodiment of this application, the pressure sensing array includes at least three high-frequency dynamic pressure sensors distributed at equal or non-equal intervals along the ash conveying pipeline, wherein the data sampling frequency of the pressure sensors is not less than 100Hz.

[0026] Specifically, in actual installation, the sensor is connected to the pipeline via a root valve with an isolation diaphragm, and installed in a straight pipe section at least five times the pipe diameter away from the flow-disrupting element to ensure the accuracy of the measurement data. By calculating the real-time pressure difference between adjacent sensors, the system can obtain the pipeline pressure gradient distribution, providing key parameters for predicting pipe blockage risks.

[0027] The sensor was installed using standard industrial methods: The sensor is connected to the pipeline via a root valve with an isolation diaphragm. The installation position is a straight pipe section with a distance of ≥5D (D is the pipe diameter) from the valve, elbow, or other flow-disrupting elements. The sensor signal is connected to the dedicated high-speed acquisition module of the control system via a shielded cable.

[0028] During implementation, the signals from each sensor are processed in the following ways: high-speed IO modules are used for synchronous acquisition, the sampling frequency is uniformly set to 100Hz, the raw data is digitally filtered, the cutoff frequency is set to 50Hz, and the pipeline pressure gradient distribution is obtained by calculating the real-time pressure difference between adjacent sensors.

[0029] In one embodiment of this application, the acoustic emission sensing array includes at least three acoustic emission sensors paired with a pressure sensor, the acoustic emission sensors having a resonant frequency range of 30kHz to 80kHz, and being attached to the outer wall of the pipe via a magnetic base.

[0030] Specifically, the sensor is directly attached to the corresponding monitoring point on the outer wall of the pipe using a magnetic base, avoiding the leakage risk and structural strength reduction caused by pipe openings. A special coupling agent is used between the sensor and the pipe wall to ensure efficient sound wave transmission. The installation location is selected at twice the pipe diameter after the bend and in areas of significant vibration in the straight pipe section.

[0031] In one embodiment of this application, the risk prediction unit calculates the blockage risk index by considering at least the following factors: The relative pressure gradient factor is calculated based on pressure sensor array data. Pressure gradient deterioration rate factor calculated based on the relative pressure gradient change rate; Local flow anomaly factor calculated based on pressure sensor array data; acoustic anomaly factor calculated based on acoustic emission sensor array data.

[0032] The risk prediction unit calculates the pipe blockage risk index according to the following specific scheme: Calculation process, relative pressure gradient factor , where L is the pipe length; Pressure gradient deterioration rate factor Take the absolute value of the rate of change within the most recent 5 seconds, and calculate the absolute value of the rate of change of F1 within a unit time (e.g., 5 seconds). Local flow anomaly factors This reflects the uniformity of the flow field; Acoustic anomaly factor , Reflecting changes in sound energy intensity, : The root mean square value of the acoustic emission signal reference : Current root mean square value of acoustic emission signal Acoustic emission frequency offset; Synthesize risk index , Function: Limits the upper limit of all calculation results to 100, making PRI a regular 0-100 exponent, which facilitates subsequent hierarchical processing.

[0033] It outputs the blockage risk index in real time within the range of 0-100. The index can be set as follows: less than 30: safe state; 30-60: warning state; 60-85: intervention state; 85: emergency state.

[0034] P1, P2, and P3 are the readings of three pressure sensors from upstream to downstream of the pipeline (in actual operation, it is not limited to three sets).

[0035] In one embodiment of this application, the adaptive intervention unit is configured to execute a tiered intervention strategy: When the pipe blockage risk index exceeds the first threshold but not the second threshold, an early warning signal is issued and system status data is recorded; When the pipe blockage risk index exceeds the second threshold but not the third threshold, an instruction is sent to the execution module to execute a primary intervention strategy including pulse oscillation program and / or reduce feed rate. When the pipe blockage risk index exceeds the third threshold, an instruction is sent to the execution module to implement an emergency intervention strategy, including increasing the system delivery pressure setpoint and forcing operation until the risk is eliminated.

[0036] Level 1: Early warning response (30 < PRI ≤ 60).

[0037] Triggering condition: The risk index exceeds the safety threshold but is in a low-risk range.

[0038] System Action: Displays a yellow warning indicator on the operation interface, and automatically records key operating parameters such as pressure and flow rate at the current moment.

[0039] Objective: To remind operators to pay attention to the system status and provide data support for subsequent analysis; Level 2: Primary intervention (60 < PRI ≤ 85).

[0040] Triggering condition: The risk index enters the medium-risk range.

[0041] System actions: Execute the pulse oscillation program: increase the intake valve opening by 15-25% from the original level, maintain for 3-5 seconds, and then return to normal.

[0042] Coordinate and control the feed rate: Temporarily reduce the feed valve opening by 8-12%.

[0043] Objective: To disrupt the initial ash plug structure by pulsating airflow, while simultaneously reducing the ash-to-air ratio and restoring normal flow.

[0044] Level 3: Emergency intervention (PRI > 85).

[0045] Triggering condition: The risk index reaches the high-risk range.

[0046] System actions: Immediately increase the delivery pressure setting by 20-30%; Force this operation to continue until the PRI value drops below 30 and remains stable for at least 10 seconds.

[0047] Objective: To take the strongest measures to unclog the pipeline and prevent a complete blockage.

[0048] In one embodiment of this application, the pulse oscillation procedure includes: controlling the opening of the intake valve to increase by 10%-30% on the original basis, maintaining it for 2-5 seconds, and then restoring it to the original opening.

[0049] Specifically, the pulse oscillation program is automatically activated when the pipe blockage risk index (PRI) is in the range of 60-85. The process is as follows: the system first reads the current opening of the intake valve as the reference value, then controls the opening to be increased by 10%-30% on the original basis, and uses the PLC to control the pneumatic actuator to maintain the increased opening for 2-5 seconds, and finally smoothly restores it to the original opening.

[0050] In one embodiment of this application, the control module adopts a hybrid architecture consisting of a programmable logic controller and an industrial computer, wherein the risk prediction unit and the adaptive intervention unit operate on the industrial computer.

[0051] Specifically, the control module adopts a hybrid architecture consisting of a programmable logic controller (PLC) and an industrial computer (ICC). The PLC is responsible for implementing rapid logic control and safety interlocks of the underlying equipment to ensure reliable operation of actuators such as valves and pumps; while complex risk prediction algorithms and adaptive intervention decisions run on the ICC platform.

[0052] In one embodiment of this application, the control module is further configured to: Based on long-term trend analysis of acoustic emission sensor data, the cumulative wear of pipe bends or specific pipe sections is calculated. When the cumulative wear exceeds a preset threshold, a maintenance reminder is generated, which includes the recommended location of the pipe section to be replaced or an estimated remaining service life.

[0053] Specifically, the system continuously collects acoustic emission signals from each pipe section and calculates the cumulative wear by analyzing the cumulative effect of signal energy levels. When the calculated value exceeds a preset threshold, a maintenance reminder is automatically generated. This reminder clearly identifies the location of the pipe section that needs maintenance and estimates the remaining service life based on the wear rate, providing a basis for predictive maintenance.

[0054] In one embodiment of this application, the control module is configured to perform wear monitoring and early warning according to the following steps: Learning period: In the initial stage of system operation, the root mean square value of acoustic emission signal of each pipe section is collected within at least 100 ash conveying cycles, and the average value of these root mean square values ​​of acoustic emission signal is calculated as the reference acoustic energy level of the pipe section. Monitoring and accumulation: In subsequent operation, the increment of the root mean square value of the acoustic emission signal relative to the reference level in each ash conveying cycle is calculated in real time, and this increment is included in the cumulative wear equivalent of the corresponding pipe section; Dynamic early warning: When the cumulative wear equivalent of a certain pipe section exceeds the first dynamic threshold, an early maintenance reminder is generated; when the cumulative wear equivalent exceeds the higher second dynamic threshold, an emergency replacement reminder is generated. The first and second dynamic thresholds are dynamically adjusted based on the historical wear rate of the pipe section and the preset safe operation cycle. The remaining service life estimate is calculated by subtracting the current cumulative wear equivalent from the maximum permissible cumulative wear equivalent, and then dividing by the recent average wear rate.

[0055] In this embodiment, the wear monitoring and early warning of the control module are implemented according to the following process: The system first establishes the reference acoustic energy level of each pipe section within the initial 100 ash conveying cycles; then, by monitoring the increment of the acoustic emission signal relative to the reference value in real time, the wear equivalent of each pipe section is continuously accumulated; when the accumulated value exceeds the threshold dynamically adjusted based on the historical wear rate, an early maintenance reminder or an emergency replacement reminder is triggered respectively; finally, based on the difference between the current wear equivalent and the maximum allowable value, combined with the recent wear rate, the remaining service life is estimated.

[0056] A method for preventing blockage in ash conveying pipelines, comprising: The dynamic pressure signal and acoustic emission signal inside the ash conveying pipeline are collected in real time by the sensing module. Based on dynamic pressure signals and acoustic emission signals, a comprehensive pipe blockage risk index is calculated through a risk prediction unit. The adaptive intervention unit automatically generates an instruction to adjust operating parameters when the blockage risk index exceeds the first preset threshold. The module executes parameter adjustment commands to reduce the risk of pipe blockage.

[0057] In actual operation, the first stage is: comprehensive status monitoring.

[0058] The system uses pressure sensor arrays and acoustic emission sensor arrays deployed at key locations in the ash conveying pipeline to collect dynamic pressure signals and acoustic emission signals in real time during the conveying process. The pressure sensor array consists of at least three high-frequency dynamic pressure sensors with a sampling frequency of not less than 100Hz to accurately monitor the pressure distribution in the pipeline; the acoustic emission sensor array uses sensors with a resonant frequency of 30-80kHz, which are attached to the outer wall of the pipeline by a magnetic base to capture the acoustic characteristics of material flow.

[0059] Phase Two: Multi-dimensional Risk Analysis.

[0060] The risk prediction unit calculates the plugging risk index (PRI) by synthesizing four key factors based on the collected sensing data: the relative pressure gradient factor reflects the overall flow resistance of the pipeline; the pressure gradient deterioration rate factor identifies the trend of ash plug formation; the local flow pattern anomaly factor locates the ash accumulation position; and the acoustic anomaly factor evaluates the change in the flow state. By weighted fusion of these factors, a PRI value ranging from 0 to 100 is generated to accurately quantify the plugging risk level.

[0061] Phase 3: Intelligent risk assessment and decision-making.

[0062] The system automatically performs hierarchical evaluation based on the PRI value: PRI ≤ 30 is the safe state; 30 < PRI ≤ 60 enters the warning state and records the operation data; 60 < PRI ≤ 85 initiates primary intervention; PRI > 85 immediately executes emergency intervention. This hierarchical mechanism ensures that the system takes corresponding countermeasures according to the severity of the risk.

[0063] Phase 4: Precise adaptive intervention.

[0064] The adaptive intervention unit outputs control instructions according to the risk assessment results: only gives a warning in the warning state; executes the pulse oscillation program (the intake valve opening is increased by 10% - 30% and maintained for 2 - 5 seconds) and coordinates to reduce the feeding amount in the primary intervention stage; immediately increases the conveying pressure by 20% - 30% and forces operation until the risk is eliminated in the emergency intervention stage. The execution module accurately implements these instructions to achieve precise regulation.

[0065] Phase 5: Predictive maintenance support.

[0066] The system establishes wear models for each pipe section by long-term monitoring of the trend of acoustic emission signals. When the cumulative wear amount exceeds the dynamically adjusted threshold, it automatically generates a maintenance reminder and provides an estimate of the remaining life, realizing the transformation from passive maintenance to predictive maintenance.

[0067] The application embodiment discloses an electronic device. Specifically, it includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps of any of the above methods.

[0068] The application embodiment discloses a computer-readable storage medium. Specifically, a computer program is stored thereon, and when the program is executed by a processor, it implements the steps of any of the above methods.

[0069] In summary, the intelligent anti-clogging, wear-reducing, and energy-saving ash conveying system of this application, through multi-sensor information fusion and intelligent algorithms, achieves precise perception of the operating status of the ash conveying pipeline and early warning of pipe blockage risks. The adopted graded intervention strategy can accurately adjust operating parameters according to the risk level, effectively avoiding excessive intervention while ensuring conveying reliability, and significantly reducing compressed air consumption and equipment wear. The predictive maintenance function provided by the system can identify pipe section wear trends in advance and optimize maintenance cycles, thereby improving overall system operating efficiency, extending equipment lifespan, and achieving comprehensive benefits of anti-clogging, wear reduction, and energy saving.

[0070] In the description of this specification, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0071] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0072] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. An intelligent anti-clogging, wear-reducing, energy-saving ash conveying system, characterized in that, include: The sensing module includes at least one pressure sensing array and at least one acoustic emission sensing array set at different positions in the ash conveying pipeline, for real-time acquisition of dynamic pressure signals and acoustic emission signals of material conveying in the pipeline; A control module, communicatively connected to the sensing module, includes: The risk prediction unit is configured to calculate a comprehensive pipe blockage risk index based on the dynamic pressure signal and the acoustic emission signal. An adaptive intervention unit is configured to automatically adjust the system's operating parameters when the blockage risk index exceeds a first preset threshold. The execution module is communicatively connected to the control module and is used to execute the parameter adjustment commands issued by the adaptive intervention unit.

2. The intelligent anti-clogging, grinding-reducing, energy-saving ash conveying system according to claim 1, characterized in that, The pressure sensing array includes at least three high-frequency dynamic pressure sensors that are equidistant or non-equidistantly distributed along the ash conveying pipeline, and the data sampling frequency of the pressure sensors is not less than 100Hz.

3. The intelligent anti-clogging, grinding-reducing, energy-saving ash conveying system according to claim 2, characterized in that, The acoustic emission sensor array includes at least three acoustic emission sensors paired with the pressure sensor. The resonant frequency range of the acoustic emission sensors is 30kHz to 80kHz, and they are attached to the outer wall of the pipe by a magnetic base.

4. The intelligent anti-clogging, grinding-reducing, energy-saving ash conveying system according to claim 3, characterized in that, When calculating the pipe blockage risk index, the risk prediction unit considers at least the following factors: The relative pressure gradient factor is calculated based on the pressure sensor array data. The pressure gradient deterioration rate factor is calculated based on the relative pressure gradient change rate. Local flow anomaly factor calculated based on the pressure sensor array data; The acoustic anomaly factor is calculated based on the acoustic emission sensor array data.

5. The intelligent anti-clogging, grinding-reducing, energy-saving ash conveying system according to claim 4, characterized in that, The adaptive intervention unit is configured to execute a tiered intervention strategy: When the blockage risk index exceeds the first threshold but does not exceed the second threshold, an early warning signal is issued and system status data is recorded; When the pipe blockage risk index exceeds the second threshold but does not exceed the third threshold, an instruction is sent to the execution module to execute a primary intervention strategy including a pulse oscillation program and / or a reduction in the feed rate. When the blockage risk index exceeds the third threshold, an instruction is sent to the execution module to execute an emergency intervention strategy, including increasing the system delivery pressure setpoint and forcing operation until the risk is eliminated.

6. The intelligent anti-clogging, grinding-reducing, energy-saving ash conveying system according to claim 5, characterized in that, The pulse oscillation procedure includes: increasing the opening of the intake valve by 10%-30% from its original position and maintaining it for 2-5 seconds, then returning it to its original opening position.

7. The intelligent anti-clogging, grinding-reducing, energy-saving ash conveying system according to claim 1, characterized in that, The control module adopts a hybrid architecture consisting of a programmable logic controller and an industrial computer, wherein the risk prediction unit and the adaptive intervention unit run on the industrial computer.

8. The intelligent anti-clogging, grinding-reducing, energy-saving ash conveying system according to claim 3, characterized in that, The control module is also configured to: Based on long-term trend analysis of the acoustic emission sensor data, the cumulative wear of pipe bends or specific pipe sections is calculated. When the cumulative wear exceeds a preset threshold, a maintenance reminder is generated, which includes a suggested replacement location for the pipe section or an estimated remaining service life.

9. The intelligent anti-clogging, grinding-reducing, energy-saving ash conveying system according to claim 8, characterized in that, The control module is configured to perform wear monitoring and early warning according to the following steps: Learning period: In the initial stage of system operation, the root mean square value of acoustic emission signal of each pipe section is collected within at least 100 ash conveying cycles, and the average value of these root mean square values ​​of acoustic emission signal is calculated as the reference acoustic energy level of the pipe section. Monitoring and accumulation: In subsequent operation, the increment of the root mean square value of the acoustic emission signal relative to the reference level in each ash conveying cycle is calculated in real time, and this increment is included in the cumulative wear equivalent of the corresponding pipe section. Dynamic early warning: When the cumulative wear equivalent of a certain pipe section exceeds the first dynamic threshold, an early maintenance reminder is generated; when the cumulative wear equivalent exceeds the higher second dynamic threshold, an emergency replacement reminder is generated. The first and second dynamic thresholds are dynamically adjusted based on the historical wear rate of the pipe section and the preset safe operation cycle. The remaining service life estimate is calculated by subtracting the current cumulative wear equivalent from the maximum permissible cumulative wear equivalent, and then dividing by the recent average wear rate.

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