Intelligent ash conveying method and system

Abstract

The application discloses an intelligent ash conveying method and system, comprising the following steps: predicting the conveying capacity and required conveying air volume of an existing ash conveying system, judging whether the fly ash conveying volume meets the requirement and whether the air compressor capacity in the existing ash conveying system meets the requirement; according to the prediction result, the existing ash conveying system is reformed, an intelligent turbulent flow air supplement valve is arranged on the ash conveying pipeline, the ash conveying system obtains the pressure, flow and output parameters of each part in real time, and the pressure parameters at the intelligent turbulent flow air supplement valve are compared to obtain the pressure loss parameters; the pneumatic ash conveying characteristic diagram of the ash conveying system is determined, intelligent optimization is carried out, and the best operation working condition point of the ash conveying system is determined; a preset fuzzy rule base is determined based on the best working condition point determined by the intelligent optimization, the input pressure loss, flow and output parameters are fuzzed, and then corresponding control instructions are output, the pump fluidization air, the ash conveying pipeline conveying air, the bin pump outlet air supplement and the intelligent turbulent flow air supplement valve air supplement are adjusted online, and the method has the characteristics of stable operation.

Description

Technical Field

[0001] This application relates to the field of pneumatic conveying technology, specifically to an intelligent ash conveying method and system. Background Technology

[0002] Ash conveying systems, also known as pneumatic ash conveying systems, are widely used in power plants and the metallurgical industry. Their working principle is to use the energy of airflow to transport granular materials (such as ash) along the airflow direction through a closed pipeline. The main components of the system include feed valves, silo pumps, and dust collectors. During the feeding stage, the ash falls into the silo pump by gravity and is then transported to the designated location by compressed air. The design and operation of this system can achieve remote monitoring and adjustment to ensure efficient material conveying.

[0003] However, the existing ash conveying methods have the following drawbacks: the existing ash conveying methods cannot predict the performance requirements of the existing ash conveying system, and at the same time, they do not have intelligent adjustment functions during the ash conveying process, and cannot achieve predictive optimization and continuous stable operation of the conveying process. Summary of the Invention

[0004] One objective of this application is to provide an intelligent ash conveying method and system that enables the system to maintain optimal operating conditions.

[0005] To achieve the above objectives, the technical solution adopted in this application is: an intelligent ash conveying method, comprising the following steps:

[0006] S100, predict the conveying capacity and required conveying air volume of the existing ash conveying system, determine whether it meets the requirements for fly ash conveying volume, and whether the air compressor capacity of the existing ash conveying system meets the requirements;

[0007] S200, based on the prediction results, the existing ash conveying system is modified, and an intelligent turbulent air supply valve is installed at a suitable location in the ash conveying pipeline. The ash conveying system is suitable for real-time acquisition of pressure, flow and output parameters of each part, and the pressure loss parameters are calculated by comparing the pressure parameters at the intelligent turbulent air supply valve.

[0008] S300 dynamically constructs and updates the pneumatic ash conveying characteristic diagram of the ash conveying system based on system operating parameters, and then performs intelligent optimization to determine the optimal operating condition point of the ash conveying system at the present time.

[0009] The S400 has a preset fuzzy rule base. Based on the optimal operating point determined by intelligent optimization, it fuzzifies the input pressure loss, flow rate and output parameters, and then outputs the corresponding control commands to adjust the pump fluidizing gas, ash conveying pipeline gas, silo pump outlet gas replenishment and intelligent turbulent flow gas replenishment valve online.

[0010] In some embodiments, in step S100, the single feed mass is first calculated based on the silo pump volume and the material bulk density, where single feed mass = silo pump volume × material bulk density. Then, the maximum time for one full pump delivery is calculated based on the system output, where maximum time = single feed mass ÷ system output. The actual delivery time is compared with the maximum time, and combined with the monitoring of changes in pressure loss value, to predict whether the system meets the requirements for fly ash delivery.

[0011] In some embodiments, in step S100, the basic gas volume requirement is first calculated based on the system output: required gas volume = system output × solid-to-gas ratio. The volumetric gas volume is then calculated by combining the density of the conveyed gas and the characteristics of the pipeline: volumetric gas volume = required gas volume ÷ gas density. Then, the maximum time for a single full-pump delivery is calculated based on the system output: maximum time = single feed mass ÷ system output. The actual delivery time is compared with the maximum time, and combined with the monitoring of changes in pressure loss value, to predict whether the system meets the requirements for fly ash delivery.

[0012] In some embodiments, in step S200, multiple intelligent turbulent air supply valves are grouped together and locally installed in multiple key sections of a single pipeline, and the spacing between each intelligent turbulent air supply valve is calculated based on the material settling velocity and the gas velocity.

[0013] In some embodiments, the key sections include the initial section of the ash conveying pipeline, the silo pump room, the horizontal section of the discharge port, and the horizontal section after vertical descent.

[0014] In some embodiments, the modification of the existing ash conveying system in step S200 includes modifying the structure of the silo pump, the diameter of the ash conveying pipeline, the air supply components, valves, and setting up an intelligent turbulent air replenishment valve.

[0015] In some embodiments, based on the pneumatic ash conveying characteristic diagram of the ash conveying system, a point that is about to be blocked and has high pressure is selected, and then multiplied by a fixed safety factor as the optimal operating condition point; when fuzzy input is used, pressure loss, flow rate and output parameters are converted into pressure loss level, flow velocity level and output magnitude.

[0016] In some embodiments, the following logic is executed during the ash conveying system adjustment: the fluidizing gas of the silo pump is adjusted to the target fluidizing pressure based on the material bulk density and feed quality; the conveying gas of the ash conveying pipeline is dynamically adjusted based on the system output and solid-to-gas ratio to maintain the target conveying pressure; when the local pressure exceeds the threshold, the silo pump outlet automatically opens to supplement 0-10% of the system gas volume, and automatically closes after 10-20 seconds; when the local pressure loss exceeds the threshold, the intelligent turbulent flow supplementing valve automatically supplements 0-10% of the system gas volume, and automatically closes when the pressure loss decreases; the intelligent turbulent flow supplementing valve supplements gas in the form of pulse jet.

[0017] In some embodiments, when predicting the performance of the ash conveying system, it is detected whether the output of coarse ash meets the requirements; the intelligent turbulent air supply valve integrates an electromagnetic actuator, a pressure sensor and a microcontroller.

[0018] An intelligent ash conveying system, which applies any of the intelligent ash conveying methods described above.

[0019] Compared with the prior art, the beneficial effects of this application are as follows: The intelligent ash conveying method and system of this application can predict the conveying capacity and required conveying gas volume of the existing system to determine whether the performance requirements of the existing system meet the requirements and whether there are original design defects. Then, the existing system can be appropriately modified to realize the application basis of intelligent ash conveying. After the modification, the system can be fuzzy controlled and adjusted according to the optimal operating condition point determined based on the pneumatic ash conveying characteristic diagram, so that the current system can always maintain the optimal operating state. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the arrangement of an intelligent turbulent air supply valve according to a preferred embodiment of this application.

[0021] Figure 2 This is a pneumatic ash conveying characteristic diagram according to a preferred embodiment of this application.

[0022] Figure 3 This is a schematic diagram of key parameters of a feature diagram according to a preferred embodiment of this application. Detailed Implementation

[0023] The present application will be further described below with reference to specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0024] In the description of this application, it should be noted that the directional terms such as "center", "lateral", "longitudinal", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", and "counterclockwise" indicate the orientation and positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. They should not be construed as limiting the specific protection scope of this application.

[0025] It should be noted that the terms "first," "second," etc., in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0026] The terms “comprising” and “having”, and any variations thereof, in the specification and claims of this application are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0027] The following description, in conjunction with the accompanying drawings, further illustrates this application:

[0028] like Figures 1 to 3 As shown, this application provides an intelligent ash conveying method, which includes the following steps.

[0029] S100 predicts the conveying capacity of the existing ash conveying system to determine whether it meets the requirements for fly ash conveying volume, predicts the required conveying air volume, and determines whether the air compressor capacity of the existing ash conveying system meets the requirements.

[0030] This step mainly provides the best application foundation for intelligent ash conveying, specifically by predicting the existing ash conveying system through material property method system design technology.

[0031] The main operating parameters of a pneumatic ash conveying system are: system output, compressed air consumption, and conveying pressure loss. The relationship between these three depends on the characteristics of the ash conveying pipeline and the fly ash being conveyed. Once the ash conveying pipeline is determined, the material properties of the fly ash (such as bulk density, particle size, and moisture content) play a key role. The material properties of the fly ash are used to infer and determine the operating parameters of the pneumatic ash conveying system, which is the material property method system design technology.

[0032] The system's conveying capacity prediction is mainly achieved by calculating the single feed mass, conveying time, and monitoring pressure loss changes. The specific process is as follows: First, the single feed mass is calculated based on the silo pump volume and material bulk density: Single feed mass = silo pump volume × material bulk density. Then, the maximum time for a full pump conveying operation is calculated based on the system output: Maximum time = Single feed mass ÷ System output. The actual conveying time is compared with the maximum time. If the actual conveying time exceeds the maximum time, the current ash conveying system capacity is insufficient. This is combined with pressure loss value monitoring to predict whether the system meets the fly ash conveying requirements.

[0033] The principle of using pressure loss changes for auxiliary prediction is as follows: For the same material and solid-gas ratio, each conveying system has an almost fixed pressure loss value. If the pressure loss changes, it indicates that the material properties or solid-gas ratio has changed. The quality of the conveying gas can be controlled and measured by a flow meter, and the quality of the material entering the pipeline from the silo pump can also be controlled by the pump start pressure and fluidizing gas flow rate. Therefore, when the pressure loss increases, we can check whether the gas quality has changed, and further infer whether the material has changed, so as to predict and optimize the conveying capacity.

[0034] In some embodiments, in step S100, the basic gas volume requirement is first calculated based on the system output: required gas volume = system output × solid-to-gas ratio. The volumetric gas volume is then calculated by combining the density of the conveyed gas and the characteristics of the pipeline: volumetric gas volume = required gas volume ÷ gas density. Then, the maximum time for a single full-pump delivery is calculated based on the system output: maximum time = single feed mass ÷ system output. The actual delivery time is compared with the maximum time. If the actual delivery time exceeds the maximum time (insufficient output) or is significantly lower than the maximum time (excessive output), the gas volume needs to be adjusted to optimize the delivery efficiency. This is combined with monitoring of changes in pressure loss to predict whether the system meets the requirements for fly ash delivery.

[0035] The principle of using pressure loss changes for auxiliary prediction is the same as above. When the material properties or solid-to-gas ratio change, the system pressure loss will deviate from the normal value. An increase in pressure loss may indicate that the material flowability has deteriorated or the solid-to-gas ratio is too high, requiring an increase in gas volume; a decrease in pressure loss may indicate that the solid-to-gas ratio is too low, requiring a reduction in gas volume. The quality of the conveyed gas is measured in real time by a flow meter, and the quality of the material is monitored by controlling the pump start-up pressure and fluidizing gas flow. The predicted instantaneous conveyed gas volume (volume gas volume) is compared with the air compressor capacity to ensure stable and efficient conveying.

[0036] It is worth noting that for pneumatic ash conveying systems, coarse ash is more difficult to convey than fine ash. Therefore, when the output requirements are the same, it is often necessary to check whether the output of coarse ash meets the requirements. Thus, when predicting the performance of the ash conveying system, it is necessary to check whether the output of coarse ash meets the requirements to ensure that the ash conveying system can meet various working conditions, thereby determining the optimization direction of the ash conveying system and the stability of the optimized operation.

[0037] S200, based on the prediction results, such as when performance requirements are not met or there are original design defects, makes appropriate modifications to the existing ash conveying system to provide the best application basis for intelligent ash conveying. Intelligent turbulent air supply valves are installed at appropriate locations in the ash conveying pipeline. The ash conveying system is suitable for real-time acquisition of pressure, flow, and output parameters of each part, and the pressure loss parameters are calculated by comparing the pressure parameters at the intelligent turbulent air supply valve.

[0038] The stable and reliable operation of a pneumatic ash conveying system mainly depends on two components: the silo pump and the ash conveying pipeline. The silo pump needs to ensure smooth and controllable material supply, while the ash conveying pipeline ensures smooth conveying. The former has a great influence on the latter. In order to ensure smooth conveying in the ash conveying pipeline, in addition to determining a reasonable pipe diameter (determined by the system design / prediction technology in S100), the fly ash in the silo pump must be fully fluidized before entering the ash conveying pipeline and enter the ash conveying pipeline in a controllable manner to prevent the material from clumping together and entering the ash conveying pipeline too quickly, which would cause the risk of pipe blockage.

[0039] Since air is compressible, the pressure at the front end of the ash conveying system (i.e., where the silo pump is located) is often high during conveying, resulting in a lower airflow velocity in the silo pump room and the initial section of the ash conveying pipeline. This makes ash-air separation more likely and increases the risk of blockage. To address this, this application installs intelligent turbulent flow air supply valves in the silo pump room and the initial section of the ash conveying pipeline. When the ash pipe is about to become blocked, the pressure rises, triggering the pressure switch of the intelligent turbulent flow air supply valve to supply air and eliminate the blockage. Once the fly ash enters the ash conveying pipeline, the conveying speed increases due to the compressibility of air, preventing pipe blockage.

[0040] Therefore, once the ash conveying system is built, the system can achieve stable operation by adjusting the fluidizing gas of the silo pump (controlling the output), the conveying gas of the ash pipe and the supplementary gas at the outlet of the tail silo pump (ensuring smooth ash pipe conveying), and controlling the air supply of the intelligent turbulent flow supplementary gas valve (eliminating possible blockages between the silo pumps and / or at the beginning of the ash pipe).

[0041] In some embodiments, multiple intelligent turbulent air supply valves are grouped together and locally installed in multiple key sections of a single pipeline, and the spacing between each intelligent turbulent air supply valve is calculated based on the material settling velocity and gas velocity.

[0042] The newly added intelligent turbulent gas injection valve induces turbulence in the ash conveying pipeline by dynamically injecting auxiliary gas (generally accounting for 0-20% of the system gas volume in this application), thereby improving the uniformity of coarse ash and fly ash mixing and conveying, and assisting in pressure loss prediction.

[0043] In some embodiments, the intelligent turbulent air supply valve integrates an electromagnetic actuator, a pressure sensor, and a microcontroller, and can automatically adjust the air volume based on pressure loss feedback.

[0044] In some embodiments, the key sections include the initial section of the ash conveying pipeline, the silo pump room, the horizontal section of the discharge port, and the horizontal section after vertical descent. Turbulent air replenishment ensures that the area where coarse ash is easily deposited is covered, reducing or avoiding the occurrence of excessively long material plugs and excessively high local pressure loss.

[0045] In some embodiments, the renovation of existing ash conveying systems can focus on modifying the structure of the silo pump, the diameter of the ash conveying pipeline (including diameter change points), the air supply components, valves, and the installation of intelligent turbulent flow replenishment valves.

[0046] S300, the core of the intelligent adjustment of air volume at various points in this application is based on the pneumatic ash conveying characteristic diagram of the ash conveying system. Therefore, it is necessary to construct and update the pneumatic ash conveying characteristic diagram of the ash conveying system. Specifically, this can be achieved by calculating based on the system operating parameters, as described in reference [reference needed]. Figure 2 As shown, intelligent optimization is then performed to determine the optimal operating point of the ash conveying system (meeting output and minimizing energy consumption).

[0047] The pneumatic ash conveying characteristic is the underlying model describing the pneumatic ash conveying process. This characteristic diagram consists of three key parameters: system output (ms), air flow rate (mf), and total pressure loss (Δp). Here, mf is the horizontal axis, and Δp is the vertical axis. On the same curve, the same value corresponds to the same value (ms). The physical meaning of the three key parameters is explained in [reference needed]. Figure 3 As shown.

[0048] In some embodiments, neural network technology can be further used to establish a nonlinear feature mapping relationship based on real-time collected operating parameters (such as pressure loss, gas flow rate, and system output), dynamically construct and update the pneumatic ash conveying characteristic diagram of the system, accurately describe the nonlinear relationship between material conveying behavior and pipeline state, realize self-learning and updating of the dynamic characteristics of the pneumatic ash conveying system, and provide real-time data support for the characteristic diagram.

[0049] It is understood that the pneumatic ash conveying characteristic diagram described in this application is a system characteristic expression diagram formed based on the operating law of the pneumatic conveying system. It is used to reflect the relationship between parameters such as pressure loss, gas flow rate, solid-gas ratio, and system output. It can comprehensively represent the gas-solid two-phase characteristic relationship of the ash conveying system under different operating conditions and is the core basis for intelligent ash conveying control. The formation of this characteristic diagram mainly comes from laboratory experiments, field operation experiments, and data inference to comprehensively obtain the characteristic laws of the pneumatic conveying system under different working conditions. At the same time, the basic physical characteristics of pneumatic conveying are universal, while the specific parameters (pipe diameter, length, material characteristics, etc.) of each system are different. Therefore, the characteristic diagram of each system has different parameter characteristics, but the formation mechanism is consistent.

[0050] Previous technologies relied heavily on human experience and static test data. This application introduces neural network and machine learning technologies on this basis, and uses real-time collected operating data (such as pressure loss, gas flow rate, system output, etc.) to dynamically construct and update the pneumatic ash conveying characteristic diagram of the system, so as to realize the adaptive and real-time evolution of the characteristic curve. This characteristic diagram is generated and updated in real time by the machine learning model, reflecting the "current operating condition visualization characteristics" of pneumatic conveying.

[0051] The theoretical basis is mentioned above: the basic gas volume requirement is calculated based on the system output, and the system gas volume can be determined by the solid-to-gas ratio, that is, the required gas mass is equal to the system output multiplied by the solid-to-gas ratio; combined with the density of the conveyed gas and the characteristics of the pipeline, the volumetric gas volume can be further calculated (the volumetric gas volume is equal to the gas mass divided by the gas density); at the same time, the longest time for a single full-pump conveying (calculated by dividing the single feed mass by the system output) can be used for verification; in actual operation, the system output can also be corrected in real time by means of weight sensors or solid flow sensors, so as to realize the closed-loop feedback of operating data and the dynamic self-iteration of the characteristic diagram; therefore, the step of dynamically constructing and updating the pneumatic ash conveying characteristic diagram of the ash conveying system based on the system operating parameters (which can be combined with neural network technology) logically plays the role of establishing the mapping relationship between the system operating parameters and the pneumatic ash conveying characteristic diagram, which is the premise support for subsequent fuzzy control and predictive optimization.

[0052] In some embodiments, based on the pneumatic ash conveying characteristic diagram of the ash conveying system, a point that is about to be blocked and has high pressure is selected, and then multiplied by a fixed safety factor as the optimal operating condition point. It can be understood that during the pneumatic ash conveying process, the air volume is small but not blocked, and once the pressure is high, it indicates that the output has increased.

[0053] The S400 has a preset fuzzy rule base. Based on the optimal operating point determined by intelligent optimization, it fuzzifies the input pressure loss, flow rate and output parameters, and outputs corresponding control commands (e.g., if the pressure loss is high and the flow rate is low, increase the amount of supplementary air). The system adjusts the pump fluidizing gas, the conveying gas in the ash conveying pipeline, the supplementary air at the outlet of the silo pump and the supplementary air at the intelligent turbulent flow valve online to ensure conveying efficiency and stability.

[0054] When fuzzifying input, pressure loss, flow rate, and output parameters are converted into pressure loss level, flow velocity level, and output magnitude.

[0055] In some embodiments, the following logic is executed during the adjustment of the ash conveying system: the fluidizing gas of the silo pump is adjusted to the target fluidizing pressure (0.15-0.2 MPa) according to the bulk density of the material and the feed quality to ensure sufficient fluidization of the material; the conveying gas of the ash conveying pipeline is dynamically adjusted according to the system output and solid-to-gas ratio to maintain the target conveying pressure (0.2-0.3 MPa); the replenishment of air at the silo pump outlet is fed back by a pressure sensor. When the local pressure exceeds the threshold, the silo pump outlet automatically opens to replenish 0-10% of the system air volume to stabilize the pressure difference, and then automatically closes after 10-20 seconds. The intelligent turbulent flow replenishment valve can be linked with the above air volume adjustment. When the local pressure loss exceeds the threshold, the intelligent turbulent flow replenishment valve automatically replenishes 0-10% of the system air volume, and then automatically closes when the pressure loss decreases, ensuring that the pressure loss fluctuation is controlled within ±5% and the conveying efficiency is improved by 10%-20%.

[0056] The intelligent turbulent air supply valve uses pulse jet injection for air supply, which has a better turbulent air supply effect and can better reduce the probability of blockage in ash conveying pipelines.

[0057] like Figure 1 As shown, this application also provides an intelligent ash conveying system, which applies an intelligent ash conveying method of any of the above embodiments.

[0058] In summary, this application provides a predictive intelligent ash conveying method and system, namely, a predictive intelligent ash conveying system overall architecture design, which achieves predictive optimization and stable control of the conveying process through the deep integration of machine learning dynamic modeling, real-time updating of characteristic maps and fuzzy control.

[0059] Specifically, "pre-" refers to the ability to anticipate and modify existing ash conveying systems to provide the best foundation for intelligent ash conveying if they do not meet design requirements. Furthermore, by analyzing the operating process of the ash conveying system, precise application scenarios are provided for intelligent ash conveying. "Intelligent" refers to intelligent adjustment, utilizing pneumatic ash conveying characteristic diagrams combined with fuzzy input control to precisely adapt to the modified ash conveying system. Without human intervention, intelligent adjustment (air volume at each air supply point) and control (air supply from intelligent turbulent flow supplementary air valves) are implemented in a timely manner according to different operating conditions, maintaining the system in its optimal operating state and achieving safety, stability, and energy saving in the ash conveying system.

[0060] The basic principles, main features, and advantages of this application have been described above. Those skilled in the art should understand that this application is not limited to the above embodiments. The embodiments and descriptions in the specification are only the principles of this application. Various changes and modifications can be made to this application without departing from the spirit and scope of this application. All such changes and modifications fall within the scope of this application as claimed. The scope of protection claimed by this application is defined by the appended claims and their equivalents.

Claims

1. An intelligent ash conveying method, characterized in that, Includes the following steps: S100, predict the conveying capacity and required conveying air volume of the existing ash conveying system, determine whether it meets the requirements for fly ash conveying volume, and whether the air compressor capacity of the existing ash conveying system meets the requirements; S200, based on the prediction results, the existing ash conveying system is modified by installing intelligent turbulent flow air supply valves at appropriate locations in the ash conveying pipeline. The ash conveying system is suitable for real-time acquisition of pressure, flow rate, and output parameters of each part, and the pressure loss parameters are calculated by comparing the pressure parameters at the intelligent turbulent flow air supply valves. Multiple intelligent turbulent flow air supply valves are installed in groups in multiple key sections of a single pipeline, and the spacing between each intelligent turbulent flow air supply valve is calculated based on the material settling velocity and gas velocity. The key sections include the initial section of the ash conveying pipeline, the silo pump room, the horizontal section of the discharge port, and the horizontal section after vertical descent. S300 dynamically constructs and updates the pneumatic ash conveying characteristic diagram of the ash conveying system based on system operating parameters, and then performs intelligent optimization to determine the optimal operating condition point of the ash conveying system at the present time. The S400, with a preset fuzzy rule base, uses the optimal operating point determined by intelligent optimization to fuzzify the input pressure loss, flow rate, and output parameters, and then outputs corresponding control commands to adjust the pump fluidizing gas, ash conveying pipeline gas, silo pump outlet replenishment gas, and intelligent turbulent flow replenishment valve online. When adjusting the ash conveying system, the following logic is executed: the silo pump fluidizing gas is adjusted to the target fluidizing pressure based on the material bulk density and feed quality; the ash conveying pipeline gas is dynamically adjusted based on the system output and solid-to-gas ratio to maintain the target conveying pressure; when the local pressure exceeds the threshold, the silo pump outlet automatically opens to replenish 0-10% of the system gas volume, and automatically closes after 10-20 seconds; when the local pressure loss exceeds the threshold, the intelligent turbulent flow replenishment valve automatically replenishes 0-10% of the system gas volume, and automatically closes when the pressure loss decreases.

2. The intelligent ash conveying method as described in claim 1, characterized in that: In step S100, the single feed mass is first calculated based on the silo pump volume and the material bulk density. Single feed mass = silo pump volume × material bulk density. Then, the maximum time for full pump delivery is calculated based on the system output. Maximum time = single feed mass ÷ system output. The actual delivery time is compared with the maximum time, and combined with the monitoring of pressure loss value changes, it is predicted whether the system meets the fly ash delivery requirements.

3. The intelligent ash conveying method as described in claim 1, characterized in that: In step S100, the basic gas volume requirement is first calculated based on the system output: required gas volume = system output × solid-to-gas ratio. The volumetric gas volume is then calculated by combining the density of the conveyed gas and the characteristics of the pipeline: volumetric gas volume = required gas volume ÷ gas density. Then, the maximum time for a full pump conveying operation is calculated based on the system output: maximum time = single feed mass ÷ system output. The actual conveying time is compared with the maximum time, and combined with the monitoring of changes in pressure loss value, to predict whether the system meets the requirements for fly ash conveying volume.

4. The intelligent ash conveying method as described in claim 1, characterized in that: In step S200, the modification of the existing ash conveying system includes modifying the structure of the silo pump, the diameter of the ash conveying pipeline, the air supply components, valves, and setting up an intelligent turbulent flow replenishment valve.

5. The intelligent ash conveying method as described in claim 1, characterized in that: Based on the pneumatic ash conveying characteristic diagram of the ash conveying system, the point that is about to be blocked and has high pressure is selected, and then multiplied by a fixed safety factor as the optimal operating condition point; when inputting fuzzy parameters, the pressure loss, flow rate and output parameters are converted into pressure loss level, flow velocity level and output magnitude.

6. The intelligent ash conveying method as described in claim 1, characterized in that: The intelligent turbulent air supply valve uses pulse jet injection for air supply.

7. The intelligent ash conveying method as described in claim 1, characterized in that: When predicting the performance of the ash conveying system, it is necessary to check whether the output of coarse ash meets the requirements; the intelligent turbulent air supply valve integrates an electromagnetic actuator, a pressure sensor and a microcontroller.

8. An intelligent ash conveying system, characterized in that: The intelligent ash conveying method described in any one of claims 1 to 7 is applied.

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