Power plant ash conveying control optimization system

Abstract

The invention discloses a power plant ash conveying control optimization system, and relates to the technical field of ash conveying control. The power plant ash conveying control optimization system comprises a power plant ash conveying control module and a power plant ash conveying control module, the efficiency analysis module is used for analyzing the preprocessed power plant ash conveying state data to obtain a power plant ash conveying efficiency correction index; the judgment analysis module is used for judging whether the power plant ash conveying efficiency correction index is within a preset correction interval or not; and the control and adjustment module is used for analyzing an ash conveying adjustment flow rate value and an ash conveying adjustment pressure value of conveyed gas in an ash conveying pipeline of the power plant when the correction index of the ash conveying efficiency of the power plant is lower than the lower limit of a preset correction interval, and performing control and adjustment. Therefore, the power plant ash conveying efficiency correction index can be accurately calculated, when the index is lower than the preset correction interval, the flow rate and the pressure value of the conveyed gas are automatically adjusted, and the conveying efficiency of the gas flow and the dust is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of ash transportation control, and particularly to an optimized system for ash transportation control in a power plant. Background Art

[0002] The ash transportation system in a power plant uses compressed air to transport the dust generated in the boiler and the electrostatic precipitator to the ash bunker or the transfer silo through pipelines. This process plays an important role in the daily operation of the power plant. However, the traditional ash transportation system often faces problems such as energy waste, system instability, and equipment wear during operation, thus affecting the overall ash transportation efficiency and the operation cost of the power plant.

[0003] In the traditional ash transportation control system, due to the lack of an accurate flow rate and pressure regulation mechanism, it is usually necessary to maintain a large margin of compressed air supply. Especially during the transportation process, the flow rate and pressure of the gas do not always match the actual transportation requirements of the dust, easily resulting in a large amount of compressed air waste. Especially when the load is low or the dust load is light, the excessive compressed air not only increases the energy consumption but also exacerbates the wear of the ash transportation pipelines and equipment.

[0004] Factors such as the physical properties of the dust, the stability of the air flow, and the roughness of the inner surface of the pipeline will all affect the transportation efficiency. The size, shape, and density of the dust particles will determine their behavior in the air flow. If the flow rate is inappropriate, the dust is likely to deposit in the pipeline, causing blockage and transportation interruption. The transportation parameters in the existing system are usually set as fixed values and it is difficult to dynamically adjust according to the real-time dust characteristics and air flow state, resulting in the occurrence of dust deposition and system failures during the transportation process.

[0005] The traditional ash transportation system has limitations in flow rate regulation, and there is a lack of an effective coordination mechanism between the flow rate of the air flow and the adhesiveness of the dust. An excessively high flow rate will cause wear of the pipeline and increase the waste of compressed air; while an excessively low flow rate will cause the dust particles to deposit in the pipeline, affecting the transportation effect. Due to the lack of precise regulation of the flow rate and the air flow, the ash transportation system often has difficulty in maintaining the best working state under different load conditions, reducing the efficiency of the entire transportation process.

[0006] The regulation of the traditional ash transportation control system relies on manual settings or preset control rules, and the automation and intelligence level of the system is relatively low. Although some modern systems have begun to adopt sensors and feedback mechanisms, due to the lack of integrated comprehensive analysis and real-time optimization and adjustment functions, it is still difficult to accurately regulate the system according to the actual working conditions and dust characteristics, easily resulting in slow system response and difficulty in adapting to complex working condition changes in real time.

[0007] Therefore, in the prior art, although the ash conveying system already has a certain degree of automatic control ability, due to the lack of precise control in aspects such as air flow regulation, dust conveying stability, and compressed air energy conservation in the system, it is often difficult to achieve the best ash conveying efficiency and energy usage efficiency.

[0008] The prior art, such as a coal-fired power plant ash conveying control optimization system disclosed in the invention patent application with the publication number of CN111596632B, is based on the existing ash conveying control system in the coal-fired power plant. It collects the pressure in the compressed air pipeline in the ash conveying system through an analog input module and transmits it to the main controller; it collects the operating states of each valve and motor in the ash conveying system through a digital input module and transmits them to the main controller; on the basis of realizing the ash conveying control of the original ash conveying control system, the main controller sets the initial working parameters and ash conveying cycle time of the ash conveying system, starts automatic ash conveying, and obtains the pressure in the compressed air pipeline, the unit load, and the pressure residence time, and then calculates a new ash conveying cycle time for the ash conveying system and generates a control instruction to be transmitted to the digital output module; the digital output module indirectly controls the motors and valves in the ash conveying system by controlling the actions of intermediate relays, reduces the supply margin of compressed air, avoids waste of compressed air, and realizes the optimized control of ash conveying.

[0009] Based on the above scheme, it is found that the limitations of the prior art at least include the following problems. First, the prior art solutions mainly rely on the regular and manual control of the compressed air system, and the degree of automation of the ash conveying process is relatively low. Especially when controlling the air supply margin of the air compressor, it is difficult to flexibly adjust in real time according to the changes in the dust conveying volume or air flow, which easily leads to excessive supply of compressed air, resulting in waste, and uneven air flow velocity is likely to occur during the conveying process, increasing pipeline wear, and further increasing the system maintenance cost and downtime, thus affecting the overall economic benefits. Second, the control system in the prior art is difficult to make precise flow and pressure adjustments according to the real-time ash conveying state data. Although the existing system can adjust the ash conveying cycle based on certain parameters, it does not consider the comprehensive influence of multiple dynamic parameters such as the adhesiveness of the conveyed dust, the dust stability, and the flow field efficiency. Therefore, in actual operation, it is easy to lead to low ash conveying efficiency under different conditions, especially when the air flow is unstable or the dust particles are heavier, making it difficult to ensure an efficient and continuous ash conveying process. Summary of the Invention

[0010] Aiming at the deficiencies of the prior art, the present invention provides a power plant ash conveying control optimization system, which solves the problems of waste of compressed air, unstable ash conveying flow velocity, and difficulty in real-time adjustment of the system existing in the prior art.

[0011] To achieve the above objectives, the present invention is implemented through the following technical solutions: A power plant ash conveying control optimization system, comprising: a data acquisition module, an efficiency analysis module, a judgment analysis module, and a control adjustment module; the data acquisition module is used to acquire the power plant ash conveying status data in real time and perform preprocessing; the efficiency analysis module is used to comprehensively analyze the preprocessed power plant ash conveying status data to obtain the power plant dust adhesion index, the power plant dust stability index, and the power plant flow field efficiency index, and perform comprehensive analysis to obtain the power plant ash conveying efficiency correction index; the judgment analysis module is used to judge whether the power plant ash conveying efficiency correction index is within a preset correction interval; the control adjustment module is used to analyze the ash conveying adjustment flow velocity value and the ash conveying adjustment pressure value of the conveying gas in the power plant ash conveying pipeline and perform control adjustment when the power plant ash conveying efficiency correction index is lower than the lower limit of the preset correction interval; wherein, the specific formula for calculating the power plant ash conveying efficiency correction index is as follows: Wherein, ShX is the power plant ash conveying efficiency correction index, HwD is the power plant dust stability index, LcX is the power plant dust stability index, NfZ is the power plant dust adhesion index, and HcM is the dust density value of the ash conveying pipeline of the power plant.

[0012] Further, the power plant ash conveying status data includes the pipeline surface roughness value, the pipeline diameter value, the secondary flow rate value of the conveying gas, the viscosity value of the conveying gas, the flow velocity value of the conveying gas, and the dust density value of the conveyed dust of the power plant ash conveying pipeline.

[0013] Further, the specific steps for obtaining the power plant dust stability index are as follows: Obtain the dynamic shape factor of the conveyed dust in the power plant ash conveying pipeline; Read the power plant dust adhesion index, and comprehensively analyze it in combination with the secondary flow rate value of the conveying gas, the flow velocity value of the conveying gas, the dust density value of the conveyed dust in the power plant ash conveying pipeline, and the dynamic shape factor of the conveyed dust in the power plant ash conveying pipeline to obtain the power plant dust stability index.

[0014] Further, the specific formula for calculating the power plant dust stability index is as follows: Wherein, HwD is the power plant dust stability index, XtY is the dynamic shape factor of the conveyed dust in the power plant ash conveying pipeline, QtL is the flow velocity value of the conveying gas in the power plant ash conveying pipeline, EcL is the secondary flow rate value of the conveying gas in the power plant ash conveying pipeline, HcM is the dust density value of the ash conveying pipeline of the power plant, and NfZ is the power plant dust adhesion index.

[0015] Further, the specific steps to obtain the power plant flow field efficiency index are as follows: Read the viscosity value of the conveying gas and the density value of the conveyed dust in the power plant ash conveying pipeline, and analyze the kinematic viscosity value of the conveying gas in the power plant ash conveying pipeline; Read the adhesion index of the conveyed dust in the power plant, and conduct a comprehensive analysis in combination with the flow velocity value of the conveying gas, the density value of the conveyed dust, and the kinematic viscosity value of the conveying gas in the power plant ash conveying pipeline to obtain the power plant flow field efficiency index.

[0016] Further, the specific formula for calculating the power plant flow field efficiency index is as follows: Where, LcX is the power plant flow field efficiency index, QtL is the flow velocity value of the conveying gas in the power plant ash conveying pipeline, QyN is the kinematic viscosity value of the conveying gas in the power plant ash conveying pipeline, ξ is the flow velocity-viscosity influence coefficient stored in the database, NfZ is the adhesion index of the conveyed dust in the power plant, and HcM is the density value of the conveyed dust in the power plant ash conveying pipeline.

[0017] Further, the specific steps to obtain the adhesion index of the conveyed dust in the power plant are as follows: Read the surface roughness value and the pipe diameter value of the power plant ash conveying pipeline, and analyze the adhesion influence factor of the power plant ash conveying pipeline; Conduct a comprehensive analysis of the adhesion influence factor of the power plant ash conveying pipeline in combination with the secondary flow value of the conveying gas and the viscosity value of the conveying gas in the power plant ash conveying pipeline to obtain the adhesion index of the conveyed dust in the power plant.

[0018] Further, the specific formulas for calculating the adhesion influence factor of the power plant ash conveying pipeline and the adhesion index of the conveyed dust in the power plant are as follows: Where, GnY is the adhesion influence factor of the power plant ash conveying pipeline, GbC is the surface roughness value of the power plant ash conveying pipeline, η 1 is the surface roughness influence coefficient stored in the database, δ 1 is the surface roughness weight coefficient stored in the database, GzJ is the pipe diameter value of the power plant ash conveying pipeline, η 2 is the pipe diameter influence coefficient stored in the database, δ 2 is the pipe diameter weight coefficient stored in the database, δ 1 +δ 2 = 1, NfZ is the adhesion index of the conveyed dust in the power plant, EcL is the secondary flow value of the conveying gas in the power plant ash conveying pipeline, and NdZ is the viscosity value of the conveying gas in the power plant ash conveying pipeline.

[0019] Furthermore, the specific steps for analyzing the ash transportation adjustment flow velocity value and the ash transportation adjustment pressure value of the conveying gas in the power plant ash transportation pipeline are as follows: Read the ash transportation efficiency correction index of the power plant, and comprehensively analyze it in combination with the ash adhesion index of the power plant for conveying dust, the conveying dust density value of the power plant ash transportation pipeline, the secondary flow value of the conveying gas in the power plant ash transportation pipeline, and the conveying gas viscosity value to obtain the ash transportation adjustment flow velocity value of the conveying gas in the power plant ash transportation pipeline; Combine the conveying gas viscosity value of the power plant ash transportation pipeline with the ash transportation efficiency correction index and the ash transportation adjustment flow velocity value of the conveying gas in the power plant ash transportation pipeline for comprehensive analysis to obtain the ash transportation adjustment pressure value of the conveying gas in the power plant ash transportation pipeline.

[0020] Furthermore, the specific formulas for calculating the ash transportation adjustment flow velocity value and the ash transportation adjustment pressure value of the conveying gas in the power plant ash transportation pipeline are as follows: Among them, TsZ is the ash transportation adjustment flow velocity value of the conveying gas in the power plant ash transportation pipeline, ShX is the ash transportation efficiency correction index of the power plant, NfZ is the ash adhesion index of the power plant for conveying dust, HcM is the conveying dust density value of the power plant ash transportation pipeline, EcL is the secondary flow value of the conveying gas in the power plant ash transportation pipeline, NdZ is the conveying gas viscosity value of the power plant ash transportation pipeline, and TyZ is the ash transportation adjustment pressure value of the conveying gas in the power plant ash transportation pipeline.

[0021] The present invention has the following beneficial effects:

[0022] (1) This power plant ash transportation control optimization system can accurately calculate the ash transportation efficiency correction index by real-time acquiring and analyzing various key parameters in the ash transportation system, such as the surface roughness of the pipeline, the pipeline diameter, the conveying gas flow velocity and the secondary flow, in combination with the ash adhesion index, the ash stability index and the flow field efficiency index. When this index is lower than the preset correction interval, the system automatically adjusts the flow velocity and pressure value of the conveying gas to ensure the conveying efficiency of the air flow and the dust. Through this precise adjustment, excessive or insufficient air flow is avoided, energy waste is reduced, and the system operation efficiency is improved. For example, in the case of strong ash adhesion, the system will automatically reduce the flow velocity to prevent dust deposition in the pipeline, thereby improving the overall conveying efficiency and saving energy.

[0023] (2) This power plant ash transportation control optimization system can flexibly adjust the supply amount of compressed air according to the ash transportation state and the characteristics of the dust by real-time adjusting the ash transportation flow velocity and pressure. In the prior art, due to the difficulty of accurately adjusting the air flow in the system, a large margin of compressed air supply often needs to be maintained to ensure the smooth transportation of dust. However, this system avoids this excessive air supply phenomenon by calculating the correction index, reduces the waste of compressed air, and thus effectively reduces the energy consumption and operation cost of the power plant. In practical applications, this energy-saving effect is particularly obvious during periods with low dust load or small air flow pressure requirements, which can greatly reduce the overall operation cost of the power plant.

[0024] (3) The ash transportation control optimization system of this power plant. During the ash transportation process, excessive air flow velocity and pressure will not only cause waste of compressed air, but may also cause excessive wear on pipelines and related equipment, increasing the system maintenance frequency and cost. Through the precise control in this system, when it detects that the air flow velocity in the pipeline is too fast or the pressure is too high, the system will automatically adjust these parameters to prevent pipeline wear and equipment damage caused by excessive air flow. Specifically, the control of the flow velocity can prevent excessive friction of the too-fast air flow on the ash transportation pipeline and equipment, reduce the downtime and maintenance cost caused by wear, and thus extend the service life of the equipment in the ash transportation system.

[0025] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a block diagram of an ash transportation control optimization system of a power plant according to the present invention.

[0027] Figure 2 It is a flowchart of the specific steps for obtaining the flow field efficiency index of a power plant in an ash transportation control optimization system of the present invention.

[0028] Figure 3 It is a flowchart of the specific steps for analyzing the ash transportation adjusted flow velocity value and ash transportation adjusted pressure value of the conveying gas in the ash transportation pipeline of a power plant in an ash transportation control optimization system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The general idea for the problems in the embodiments of this application is as follows:

[0030] The data acquisition module collects the ash transportation status data of the power plant in real time, including key parameters such as pipeline surface roughness, pipeline diameter, conveying gas flow velocity, gas viscosity, secondary flow rate, and dust density. All the data will be preprocessed to provide a basis for subsequent analysis. Next, in the efficiency analysis module, the preprocessed data is comprehensively analyzed to obtain the dust adhesion index, dust stability index, and flow field efficiency index of the power plant. Then, these indexes will be further combined to calculate the ash transportation efficiency correction index of the power plant. Finally, the judgment and analysis module will judge whether it is within the preset correction interval according to the ash transportation efficiency correction index of the power plant. If it is lower than the lower limit, the control and adjustment module will adjust the flow velocity and pressure value of the conveying gas according to the comprehensive analysis result to optimize the ash transportation process and achieve efficient transportation.

[0031] Please refer to Figure 1, an embodiment of the present invention provides a technical solution: a power plant ash conveying control optimization system, including: a data acquisition module, an efficiency analysis module, a judgment analysis module, and a control adjustment module; the data acquisition module is used to obtain the power plant ash conveying status data in real time and perform preprocessing; the efficiency analysis module is used to comprehensively analyze the preprocessed power plant ash conveying status data to obtain the power plant dust adhesion index, the power plant dust stability index, and the power plant flow field efficiency index (which refers to the influence degree of the flow field on the air flow and dust conveying), and perform comprehensive analysis to obtain the power plant ash conveying efficiency correction index; the judgment analysis module is used to judge whether the power plant ash conveying efficiency correction index is within a preset correction interval; the control adjustment module is used to analyze the ash conveying adjustment flow velocity value and the ash conveying adjustment pressure value of the conveying gas in the power plant ash conveying pipeline and perform control adjustment when the power plant ash conveying efficiency correction index is lower than the lower limit of the preset correction interval.

[0032] Among them, the specific formula for calculating the power plant ash conveying efficiency correction index is as follows: Among them, ShX is the power plant ash conveying efficiency correction index, HwD is the power plant dust stability index, LcX is the power plant dust stability index, NfZ is the power plant dust adhesion index (i.e., the adhesion force index between the conveyed dust and the ash conveying pipeline), and HcM is the conveyed dust density value of the power plant ash conveying pipeline.

[0033] The power plant ash conveying status data includes the pipeline surface roughness value, pipeline diameter value, secondary flow rate value of the conveying gas, viscosity value of the conveying gas, flow velocity value of the conveying gas, and conveyed dust density value of the power plant ash conveying pipeline.

[0034] Among them, the pipeline surface roughness value of the power plant ash conveying pipeline can be obtained through actual measurement or by using standardized pipeline surface roughness data. The measurement usually uses equipment such as laser scanning and surface roughness measuring instruments, or refers to the pipeline specifications and material data provided by the pipeline manufacturer. It is an important factor affecting fluid flow. A rougher pipeline inner wall will increase the frictional resistance of the air flow, easily cause air flow disorder, and affect the dust conveying efficiency. A lower roughness can reduce energy loss and dust deposition, thereby improving the conveying efficiency.

[0035] The pipeline diameter value of the power plant ash conveying pipeline can be obtained by referring to the pipeline design drawings or directly measuring the diameter of the pipeline. It directly affects the flow velocity of the air flow, pressure loss, and fluid flow characteristics. A larger pipeline diameter can reduce the resistance of the air flow and increase the air flow velocity, thereby helping the dust to be conveyed more easily. However, the diameter of the pipeline also needs to be matched with other parameters (such as flow velocity, fluid density, etc.) to ensure the best conveying efficiency.

[0036] The secondary flow rate value of the conveying gas in the power plant ash conveying pipeline can be measured and calculated through flow meters, pressure sensors, and flow field simulations. By actually monitoring the changes in the airflow in the pipeline, especially the patterns of secondary flows such as eddies and backflows, which reflect the eddies or irregular flows in the fluid and will affect the stability of the airflow. A larger secondary flow rate is likely to cause the airflow to be unstable, thus affecting the ash conveying effect. Controlling the secondary flow rate can help optimize the stability of the airflow and thereby improve the conveying efficiency.

[0037] The viscosity value of the conveying gas in the power plant ash conveying pipeline can be measured through experiments or found in fluid mechanics literature. For common gases (such as air, steam, etc.), standard physical constants and formulas can be used to calculate or find the viscosity value considering the effects of temperature and pressure. It is a key parameter describing the internal frictional resistance of the airflow. A higher viscosity usually means greater flow resistance, which is likely to affect the flow velocity and conveying effect of the airflow. By measuring and controlling the viscosity of the conveying gas, the fluidity of the airflow can be optimized and the conveying efficiency can be improved.

[0038] The flow velocity value of the conveying gas in the power plant ash conveying pipeline can be measured in real time through devices such as flow velocity meters, pitot tubes, and hot-wire anemometers. In addition, the flow velocity can also be calculated indirectly through the relationship between the flow rate and the cross-sectional area of the pipeline. It is a key factor affecting the airflow's ability to carry dust particles. A higher flow velocity helps the dust particles to better suspend and be conveyed, preventing deposition. However, too high a flow velocity is likely to cause airflow instability and energy waste. Therefore, it is necessary to accurately control the flow velocity according to the system's requirements to ensure the best conveying effect.

[0039] The density value of the conveying dust in the power plant ash conveying pipeline can be measured through experiments on dust samples (such as using a densitometer) or estimated based on the composition and physical properties of the dust. Different types of dust have different densities, and it is necessary to measure according to the actual situation or refer to relevant materials. It determines the deposition and suspension properties of the dust in the fluid. A higher density may cause the dust to easily deposit in the pipeline, affecting the conveying efficiency; while a lower density makes it easier for the dust to suspend in the airflow. Therefore, accurately understanding and controlling the dust density is crucial for optimizing the conveying system.

[0040] Specifically, the specific steps to obtain the stability index of the conveying dust in the power plant are as follows: Obtain the dynamic shape factor of the conveying dust in the power plant ash conveying pipeline; Read the adhesion index of the conveying dust in the power plant, and conduct a comprehensive analysis in combination with the secondary flow rate value of the conveying gas, the flow velocity value of the conveying gas, the density value of the conveying dust, and the dynamic shape factor of the conveying dust in the power plant ash conveying pipeline to obtain the stability index of the conveying dust in the power plant.

[0041] Among them, the specific steps to obtain the dynamic shape factor of the conveying dust in the power plant ash conveying pipeline are as follows:

[0042] First, determine the physical properties of dust particles, including:

[0043] Particle size: That is, the diameter of dust particles, usually described by particle size distribution. The size of dust particles has an important impact on their movement in the air flow.

[0044] Particle shape: The shape of dust particles affects their movement trajectories in the air flow. Non-spherical particles (such as irregularly shaped particles) usually affect the stability and efficiency of the flow.

[0045] Particle density: The density of dust particles affects their suspension and transportation in the fluid.

[0046] Secondly, analyze the characteristics of the conveying air flow (the flow state of the fluid, such as laminar flow or turbulent flow, will affect the movement of dust particles), including:

[0047] Flow velocity: The velocity of the air flow is an important factor affecting whether dust particles can be conveyed. When the flow velocity is low, particles are prone to deposition; when the flow velocity is high, particles are prone to suspension.

[0048] Fluid viscosity: The viscosity of the fluid determines the stability of the air flow and its ability to carry dust.

[0049] Flow field stability: Secondary flows such as eddies and recirculation in the flow field affect the stability of dust particles.

[0050] Then, consider the surface characteristics of the pipeline, including: The roughness of the inner wall of the pipeline and the diameter of the pipeline will also affect the movement of dust particles. A relatively rough inner wall of the pipeline will increase the probability of dust deposition.

[0051] Finally, calculate the dynamic form factor by combining the above factors (it is a parameter that comprehensively considers the physical properties of dust particles, the characteristics of the flow field, and the surface characteristics of the pipeline, and it affects the stability of dust particles in the fluid): Among them, XtY is the dynamic form factor for conveying dust in the ash conveying pipeline of the power plant, HcM is the density value of the conveyed dust in the ash conveying pipeline of the power plant, QtL is the flow velocity value of the conveying gas in the ash conveying pipeline of the power plant, EcL is the secondary flow value of the conveying gas in the ash conveying pipeline of the power plant, and NfZ is the adhesion index of the conveyed dust in the power plant.

[0052] The specific formula for calculating the stability index of the conveyed dust in the power plant is as follows: Among them, HwD is the stability index of the conveyed dust in the power plant, XtY is the dynamic form factor for conveying dust in the ash conveying pipeline of the power plant, QtL is the flow velocity value of the conveying gas in the ash conveying pipeline of the power plant, HcL is the secondary flow value of the conveying gas in the ash conveying pipeline of the power plant, HcM is the density value of the conveyed dust in the ash conveying pipeline of the power plant, and NfZ is the adhesion index of the conveyed dust in the power plant.

[0053] In this implementation scheme, by obtaining the physical properties of dust particles (such as particle size, shape, density) and analyzing the characteristics of the conveying air flow (such as flow velocity, fluid viscosity, flow field stability), this scheme can accurately evaluate whether the dust can remain stably suspended during the conveying process. The shape and density of dust particles directly affect their behavior in the air flow, and the flow velocity and air flow viscosity will determine whether the particles deposit or are conveyed. Therefore, considering these factors comprehensively helps to adjust the system parameters in real time to ensure that the dust particles can pass through the ash conveying pipeline stably and evenly, avoiding dust deposition or blockage caused by unstable air flow, thereby optimizing the ash conveying efficiency. By combining the flow field characteristics and the pipeline surface characteristics, the dynamic shape factor can be calculated to comprehensively evaluate the stability of the air flow and the behavior of dust particles in the pipeline. For example, secondary flows such as eddies and backflows in the flow field will affect the suspension and conveying of dust, and the roughness and diameter of the pipeline inner wall will affect the dust deposition probability. By accurately calculating these influencing factors, the system can more effectively optimize the flow velocity and pressure of the conveying gas, reduce pipeline wear, blockage and energy waste, and improve the stability and efficiency of system operation. This scheme dynamically adjusts the ash conveying system by real-time monitoring and analyzing various parameters (such as dust adhesion index, flow velocity, flow field stability, etc.), which not only improves the automation level of the ash conveying process but also realizes intelligent control. The system can automatically adjust the flow velocity and pressure when the stability of dust particles decreases or the air flow is unstable, avoiding the delay and inaccuracy of manual adjustment, thereby optimizing the response time and operation efficiency of the ash conveying system. By reducing dust deposition and improving air flow stability, the system can effectively reduce the wear of the ash conveying pipeline, reduce the maintenance frequency and maintenance cost of equipment. Precise dust particle conveying management avoids pipeline wear and compressed air waste caused by too fast or unstable air flow, prolongs the service life of the ash conveying system. The maintenance of pipelines and equipment reduces the overall cost and also reduces the system downtime, thereby improving the production efficiency of the power plant. This scheme effectively avoids the supply of excessive compressed air by precisely controlling the flow velocity and pressure of the conveying gas, reducing unnecessary energy waste. At the same time, the stable ash conveying process reduces the dust deposition problem and improves the overall energy efficiency of the system, which not only helps to reduce the energy cost of the power plant but also reduces environmental pollution and secondary pollution, enhancing the environmental friendliness of the power plant.

[0054] Specifically, as Figure 2 shown, the specific steps to obtain the flow field efficiency index of the power plant are as follows: Read the viscosity value of the conveying gas and the density value of the conveyed dust in the ash conveying pipeline of the power plant, and analyze the kinematic viscosity value of the conveying gas in the ash conveying pipeline of the power plant (i.e., kinematic viscosity value of conveying gas = viscosity value of conveying gas / density value of conveyed dust); Read the dust adhesion index of the power plant, and conduct a comprehensive analysis in combination with the flow velocity value of the conveying gas, the density value of the conveyed dust, and the kinematic viscosity value of the conveying gas in the ash conveying pipeline of the power plant to obtain the flow field efficiency index of the power plant.

[0055] The specific formula for calculating the flow field efficiency index of a power plant is as follows: Among them, LcX is the flow field efficiency index of the power plant, QtL is the conveying gas flow velocity value of the ash conveying pipeline of the power plant, QyN is the conveying gas kinematic viscosity value of the ash conveying pipeline of the power plant, ξ is the flow velocity-viscosity influence coefficient stored in the database, NfZ is the dust adhesion index of the power plant conveying, and HcM is the conveying dust density value of the ash conveying pipeline of the power plant.

[0056] It should be explained that the specific steps for obtaining the flow velocity-viscosity influence coefficient ξ stored in the database are as follows: It is obtained through a large number of experimental data or derivation of fluid dynamics models. By actually measuring the gas viscosity and flow velocity data under different conditions (such as temperature, pressure, and flow velocity, etc.), and performing regression analysis or using computational fluid dynamics (CFD) simulation, an empirical relationship or mathematical model between flow velocity and viscosity is obtained, which is usually stored in a database and retrieved and applied according to different operating conditions (such as gas flow temperature, pressure, pipeline characteristics, etc.). As an influence coefficient, ξ is used to quantitatively describe the combined influence of flow velocity and gas viscosity on the flow of the ash conveying system. It helps to adjust the gas flow state according to different operating conditions in actual operation and optimize the ash conveying efficiency.

[0057] In this implementation plan, through a comprehensive analysis of parameters such as gas viscosity, dust density, flow velocity, and kinematic viscosity of the gas in the ash conveying pipeline of the power plant, this plan can accurately calculate the flow field efficiency index (FEI) of the power plant. This index comprehensively considers the airflow stability, smoothness of dust transportation, and flow resistance, thereby optimizing the airflow distribution and dust transportation. Precise flow field efficiency analysis can effectively avoid dust deposition or uneven transportation caused by unstable flow, ensure uniform distribution of dust in the pipeline, and improve transportation efficiency. By obtaining the kinematic viscosity of the gas and combining it with the adhesion index of the transported dust, the stability of the airflow and the fluidity of the dust can be evaluated in real time during system operation. Kinematic viscosity is an important indicator of the flow resistance of a fluid, and its relationship with flow velocity and dust density directly affects the transportation effect of the airflow and dust. By calculating the flow field efficiency index, the system can adjust the flow velocity and pressure of the transported gas according to the actual operating conditions, reduce energy waste and equipment wear caused by too fast or too slow airflow, and improve the overall transportation effect. By comprehensively analyzing factors such as flow velocity, gas viscosity, and dust density, optimizing airflow control, and avoiding the negative impact of too fast or too slow airflow on ash transportation efficiency, the system can intelligently adjust the airflow state to reduce the waste of compressed air while meeting the ash transportation requirements, especially in the case of low dust load or low airflow requirements. By dynamically adjusting the flow velocity and pressure, unnecessary energy consumption is avoided, thus achieving an energy-saving effect. Using the flow velocity-viscosity influence coefficient stored in the database, the system can perform intelligent adjustment according to different working conditions (such as airflow temperature, pressure, etc.). This real-time control based on big data and fluid dynamics models greatly improves the automation level of the ash transportation system. Traditional manual adjustment methods may have delays and inaccuracies, while this plan ensures that the system always operates in the best state through real-time monitoring and adjustment, improving the overall efficiency and reliability. By optimizing the airflow and dust transportation process, reducing pipeline wear and equipment damage caused by unstable airflow or too high flow velocity, the system operates more smoothly, reducing the maintenance frequency and downtime. By precisely controlling the transportation conditions, the system's pressure and load requirements for equipment are more reasonable, thereby reducing the incidence of equipment failures, extending the service life of the ash conveying pipeline and related equipment, and reducing maintenance costs and equipment replacement frequencies. Since factors such as airflow temperature, pressure, and dust load change at different times and operating conditions, traditional fixed-parameter control methods often cannot cope with this change. By using the flow velocity-viscosity influence coefficient and combining it with real-time measurement data, this plan can dynamically adjust the transportation conditions to ensure stable operation of the system under various working conditions. This enables the power plant ash transportation system to better cope with changes in different loads, climatic conditions, and other external factors, providing a more flexible control plan.

[0058] Specifically, the specific steps to obtain the dust adhesion index of the power plant transportation are as follows: Read the pipe surface roughness value and pipe diameter value of the ash transportation pipeline of the power plant, and analyze the adhesion influence factors of the ash transportation pipeline of the power plant; Combine the adhesion influence factors of the ash transportation pipeline of the power plant with the secondary flow value of the transportation gas and the viscosity value of the transportation gas of the ash transportation pipeline of the power plant for comprehensive analysis to obtain the dust adhesion index of the power plant transportation.

[0059] The specific formulas for calculating the adhesion influence factor of the ash transportation pipeline of the power plant and the dust adhesion index of the power plant transportation are as follows: Among them, GnY is the adhesion influence factor of the ash transportation pipeline of the power plant, GbC is the pipe surface roughness value of the ash transportation pipeline of the power plant, η 1 is the pipe surface roughness influence coefficient stored in the database, δ 1 is the pipe surface roughness weight coefficient stored in the database, GzJ is the pipe diameter value of the ash transportation pipeline of the power plant, η 2 is the pipe diameter influence coefficient stored in the database, δ 2 is the pipe diameter weight coefficient stored in the database, δ 1 +δ 2 = 1, NfZ is the dust adhesion index of the power plant transportation, EcL is the secondary flow value of the transportation gas of the ash transportation pipeline of the power plant, and NdZ is the viscosity value of the transportation gas of the ash transportation pipeline of the power plant.

[0060] It should be explained that the specific acquisition steps of the pipe surface roughness influence coefficient η 1 stored in the database are as follows: Usually obtained through a large amount of experimental data or numerical simulation. By experimentally measuring the influence of pipes with different roughnesses on fluid flow, especially under different flow velocities and different gas types, obtain the influence of the pipe surface roughness on the frictional force and flow resistance generated by the air flow. Then, establish a mathematical relationship between roughness and flow resistance through regression analysis to obtain this influence coefficient. This coefficient reflects the contribution of the pipe surface roughness to the flow resistance. The greater the roughness, the greater the resistance suffered by the air flow. Through this coefficient, the influence of roughness on the flow characteristics of the ash transportation pipeline can be quantitatively evaluated in the model.

[0061] The specific acquisition steps of the pipe surface roughness weight coefficient δ 1 stored in the database are as follows: Usually also obtained through experimental or numerical simulation methods. In the experiment or simulation, by comparing the influence of pipes with different roughnesses on the air flow, calculate the relative weight of the roughness influence. The acquisition of this coefficient usually involves multiple factors in pipe flow, especially the influence of roughness on the transition of the air flow regime (such as laminar flow and turbulent flow). This coefficient is used to represent the contribution degree of roughness to the air flow characteristics. When calculating the total flow resistance, the roughness weight coefficient is used to quantify the proportion of roughness in the total resistance.

[0062] The influence coefficient η of the pipe diameter stored in the database 2 The specific acquisition steps are as follows: Obtained through experiments and numerical simulations. By studying the flow characteristics of airflows in pipes of different diameters, especially data such as flow velocity and pressure loss, the relationship between the pipe diameter and air flow resistance is established. This is usually determined by changing the pipe diameter and measuring the airflow changes. This influence coefficient represents the influence of the pipe diameter on the airflow. The larger the diameter, the smaller the flow resistance of the airflow. Therefore, this coefficient helps to quantify the influence of the pipe diameter on the flow of the transported gas.

[0063] The weight coefficient δ of the pipe diameter stored in the database 2 The specific acquisition steps are as follows: Obtained by analyzing the performance data of pipe systems with different diameters. Through experiments or simulations, the weighted effect of airflow in pipes with different diameters is calculated to obtain the influence degree of the pipe diameter. This coefficient is used to represent the relative weight of the pipe diameter in the entire flow system. When calculating the total flow resistance of the pipe, this weight coefficient can help the model more accurately reflect the influence of the pipe diameter on the flow characteristics.

[0064] In this implementation plan, by comprehensively analyzing factors such as the surface roughness of the ash conveying pipeline in the power plant, the pipeline diameter, the secondary gas flow rate, and the gas viscosity, it is possible to accurately evaluate the adhesion degree between the dust and the pipeline surface in the conveying pipeline. The calculation of the dust adhesion index is based on multiple influencing factors, including the pipeline surface roughness, pipeline diameter, secondary flow rate, etc. These factors jointly determine whether dust particles are likely to deposit on the inner wall of the pipeline, thus affecting the efficiency of the entire conveying system. Through precise calculation, the system can dynamically optimize the flow rate and pressure, reduce dust deposition, and ensure the stable operation of the conveying system. The influence coefficients of the pipeline surface roughness and pipeline diameter stored in the database provide a quantitative tool for pipeline optimization. These coefficients are obtained through a large number of experiments or numerical simulations and can help us accurately quantify the influence of pipeline characteristics on the airflow. For example, a rougher pipeline surface will increase the frictional resistance of the airflow, resulting in energy waste and unstable airflow, while a larger pipeline diameter can reduce the flow resistance and help the airflow transport dust more stably. By accurately obtaining these influence coefficients, the system can dynamically adjust the airflow parameters according to different pipeline conditions during operation, thereby optimizing the conveying efficiency. By combining multiple data such as pipeline surface roughness, pipeline diameter, flow rate, and gas viscosity, the system can accurately evaluate the airflow resistance and flow efficiency under different operating conditions. The influence coefficients of the pipeline surface roughness and pipeline diameter help quantify the contribution of these factors to the flow. By adjusting the flow rate and pressure, over-air supply and unstable airflow are avoided, thereby reducing energy waste and improving the overall conveying efficiency. For example, when the pipeline surface is relatively rough, the system will automatically adjust the airflow conditions to avoid excessive use of compressed air and reduce the energy consumption during system operation. This plan uses the influence coefficients stored in the database from experimental data and numerical simulation results, enabling the system to automatically adjust the operating parameters according to the real-time monitored pipeline and airflow conditions. This method is more accurate and intelligent than traditional manual adjustment and can respond in real time to changes under different operating conditions, such as temperature, pressure, dust load, etc. Through intelligent adjustment, the system can not only optimize energy use but also improve the stable conveying effect of dust, avoid the errors of manual adjustment, and enhance the overall automation level. By reducing unnecessary compressed air and overly fast airflow, this plan helps reduce pipeline wear problems caused by excessive airflow rates or unstable flows. A relatively rough pipeline surface will increase the resistance of the airflow, resulting in unstable airflow and pipeline wear. By optimizing these factors, pipeline damage can be effectively reduced, the service life of the equipment can be extended, the maintenance frequency and downtime can be reduced. By controlling the influence of factors such as pipeline diameter and roughness, the system can significantly reduce the maintenance cost of the ash conveying system. The analysis and calculation of the adhesion influence factor can play an important role in the suspension state of dust particles and the stability of the airflow. By avoiding the deposition of dust particles in the pipeline, the continuity and stability of the system are ensured, and the fault shutdown caused by dust blockage is reduced. In addition, the system adjusts the parameters in real time according to different working conditions,Enable the system to stably and effectively transport dust under different dust loads and airflow conditions, avoiding instability during the dust transportation process.

[0065] Specifically, as Figure 3 shown, the specific steps for analyzing the ash transportation adjustment flow velocity value and ash transportation adjustment pressure value of the transportation gas in the power plant ash transportation pipeline are as follows: Read the power plant ash transportation efficiency correction index, and comprehensively analyze it in combination with the power plant transportation dust adhesion index, the transportation dust density value of the power plant ash transportation pipeline, the secondary flow value of the transportation gas in the power plant ash transportation pipeline, and the transportation gas viscosity value to obtain the ash transportation adjustment flow velocity value of the transportation gas in the power plant ash transportation pipeline; Combine the transportation gas viscosity value of the power plant ash transportation pipeline with the power plant ash transportation efficiency correction index and the ash transportation adjustment flow velocity value of the transportation gas in the power plant ash transportation pipeline for comprehensive analysis to obtain the ash transportation adjustment pressure value of the transportation gas in the power plant ash transportation pipeline.

[0066] The specific formulas for calculating the ash transportation adjustment flow velocity value and ash transportation adjustment pressure value of the transportation gas in the power plant ash transportation pipeline are as follows: Among them, TsZ is the ash transportation adjustment flow velocity value of the transportation gas in the power plant ash transportation pipeline, ShX is the power plant ash transportation efficiency correction index, NfZ is the power plant transportation dust adhesion index, HcM is the transportation dust density value of the power plant ash transportation pipeline, EcL is the secondary flow value of the transportation gas in the power plant ash transportation pipeline, NdZ is the transportation gas viscosity value of the power plant ash transportation pipeline, and TyZ is the ash transportation adjustment pressure value of the transportation gas in the power plant ash transportation pipeline.

[0067] In this implementation plan, by comprehensively analyzing the ash transportation efficiency correction index, dust adhesion index, dust density, secondary flow rate, and gas viscosity of the power plant, the flow velocity and pressure of the conveying gas can be accurately calculated and dynamically adjusted. This can ensure that the air flow reaches the optimal speed and pressure during the transportation process, thereby improving the dust transportation efficiency, reducing the instability of the air flow and the deposition of dust. Precise adjustment of the flow velocity and pressure can ensure that dust particles will not deposit due to too slow air flow, nor will they cause pipeline wear and compressed air waste due to too fast air flow. By optimizing the flow velocity and pressure, excessive consumption of compressed air and energy is avoided. Traditional ash transportation systems may need to maintain a relatively high flow velocity and pressure to ensure the smooth transportation of dust, but this often leads to excessive energy consumption and waste of compressed air. However, this solution realizes precise control of the air flow through dynamic adjustment based on the correction index and multiple parameters, can flexibly adjust the flow velocity and pressure of the conveying gas according to the actual situation, avoid unnecessary energy consumption, and reduce the operating cost of the power plant. During the ash transportation process, the air flow conditions will change due to various factors (such as dust load, gas properties, pipeline characteristics, etc.). This solution can adaptively adjust the flow velocity and pressure of the conveying gas by obtaining data in real time and analyzing multiple factors, ensuring that the system can operate stably under different working conditions. This intelligent control enables the system to adapt to various working condition changes, such as fluctuations in dust load and changes in air flow pressure, ensuring the continuous, efficient, and stable operation of the system. By precisely controlling the flow velocity and pressure, the system can avoid excessive wear on pipelines and equipment caused by too fast air flow or unstable flow. For example, too fast air flow may cause damage to the inner wall of the pipeline, increasing the maintenance frequency and replacement cost. By optimizing the flow velocity and pressure, this kind of wear can be reduced, the service life of the equipment can be extended, and the reduction of system failures and maintenance can also reduce the downtime of the power plant and improve the overall production efficiency. This solution combines multiple real-time data analyses and can automatically adjust the air flow parameters according to different operating conditions. The adjustment of traditional ash transportation systems usually relies on manual settings and fixed parameters, which are easily affected by human factors and environmental changes. Through dynamic calculation and automatic adjustment, the system can accurately control the flow velocity and pressure according to real-time operation data, improve the intelligent level of the ash transportation process, reduce manual intervention, and ensure the accuracy and flexibility of operations. By calculating and adjusting parameters such as dust adhesion, dust stability, and flow field efficiency index, this solution can ensure the stable suspension of dust particles and avoid the decrease in transportation efficiency caused by dust deposition. The system can automatically adjust the flow velocity and pressure of the air flow according to real-time feedback, so that the dust always remains in the air flow, avoiding deposition and blockage problems. This is of great significance for ensuring the efficient operation of the system and reducing the failure rate.

[0068] When the ash transportation efficiency correction index of the power plant is lower than the lower limit of the preset correction range, a specific implementation example for analyzing the ash transportation adjustment flow velocity value and the ash transportation adjustment pressure value of the transportation gas in the ash transportation pipeline of the power plant is as follows. The following data is available:

[0069] The ash transportation efficiency correction index ShX of the power plant is: 0.85.

[0070] The dust adhesion index NfZ of the power plant for transportation is: 0.6.

[0071] The dust density value HcM of the ash transportation pipeline of the power plant is: 1.3 g / cm 3 (i.e., 1300 kg / m 3 )

[0072] The secondary flow rate value EcL of the transportation gas in the ash transportation pipeline of the power plant is: 0.15 m 3 / s.

[0073] The viscosity value NdZ of the transportation gas in the ash transportation pipeline of the power plant is: 0.02 Pa·s.

[0074] Substitute the above data into the specific formulas for calculating the ash transportation adjustment flow velocity value and the ash transportation adjustment pressure value of the transportation gas in the ash transportation pipeline of the power plant, and we get:

[0075] The ash transportation adjustment flow velocity value of the transportation gas in the ash transportation pipeline of the power plant = (0.85 * (1 + (0.6 / 1300))) / (1 + (0.15 / 0.02)) = (0.85 * (1 + 0.0004615)) / 8.5 = 0.8503948 / 8.5 ≈ 0.10006 m / s.

[0076] The ash transportation adjustment pressure value of the transportation gas in the ash transportation pipeline of the power plant = (0.02 * 0.10006)

[0077] / 0.85 = 0.0020012 / 0.85 ≈ 0.00235 Pa.

[0078] In this embodiment, through calculation, the ash transportation adjustment flow velocity value of the transportation gas in the ash transportation pipeline of the power plant is 0.10006 m / s, and the ash transportation adjustment pressure value is 0.00235 Pa.

[0079] In summary, the present application has at least the following effects:

[0080] By obtaining and analyzing various key parameters in the ash conveying system in real time, such as the surface roughness of the pipeline, the pipeline diameter, the flow velocity of the conveying gas, and the secondary flow rate, etc., and combining the ash adhesion index, the ash stability index, and the flow field efficiency index, the ash conveying efficiency correction index of the power plant can be accurately calculated. When this index is lower than the preset correction range, the system automatically adjusts the flow velocity and pressure value of the conveying gas to ensure the conveying efficiency of the air flow and the ash. Through this precise adjustment, excessive or insufficient air flow is avoided, energy waste is reduced, and the system operation efficiency is improved. For example, in the case of strong ash adhesion, the system will automatically reduce the flow velocity to prevent the ash from depositing in the pipeline, thereby improving the overall conveying efficiency and saving energy.

[0081] By adjusting the ash conveying flow velocity and pressure in real time, the system can flexibly adjust the supply amount of compressed air according to the ash conveying state and the characteristics of the ash. In the prior art, since it is difficult for the system to accurately adjust the air flow, a large margin of compressed air supply often needs to be maintained to ensure the smooth conveying of the ash. However, this system avoids this excessive air supply phenomenon by calculating the correction index, reduces the waste of compressed air, and thus effectively reduces the energy consumption and operation cost of the power plant. In practical applications, this energy-saving effect is particularly obvious during periods when the ash load is low or the air flow pressure requirement is small, which can greatly reduce the overall operation cost of the power plant.

[0082] During the ash conveying process, excessive air flow velocity and pressure will not only cause waste of compressed air, but may also cause excessive wear on the pipeline and related equipment, increasing the system maintenance frequency and cost. Through the precise control in this system, when it is detected that the air flow velocity in the pipeline is too fast or the pressure is too high, the system will automatically adjust these parameters to prevent pipeline wear and equipment damage caused by excessive air flow. Specifically, the control of the flow velocity can prevent the excessive friction generated by the too-fast air flow on the ash conveying pipeline and equipment, reduce the downtime and maintenance cost caused by wear, and thus extend the service life of the ash conveying system equipment.

[0083] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be construed as including the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0084] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A power plant ash handling control optimization system, characterized in that: include: Data acquisition module, efficiency analysis module, judgment analysis module, control adjustment module; The data acquisition module is used to acquire the power plant ash transport status data in real time and perform preprocessing; The efficiency analysis module is used to conduct a comprehensive analysis on the pre-processed power plant ash conveying state data to obtain the power plant conveying dust adhesion index, the power plant conveying dust stability index, the power plant flow field efficiency index, and conduct a comprehensive analysis to obtain the power plant ash conveying efficiency correction index; The judgment and analysis module is used to judge whether the power plant ash transport efficiency correction index is within a preset correction range; The control and adjustment module is used to analyze the ash conveying adjustment flow rate value and ash conveying adjustment pressure value of the conveying gas in the ash conveying pipeline of the power plant and perform control and adjustment when the ash conveying efficiency correction index of the power plant is lower than the preset lower limit of the correction interval; Among them, the specific formula for calculating the power plant ash transport efficiency correction index is as follows: Among them, ShX, HwD, LcX, NfZ, and HcM are the power plant ash conveying efficiency correction index, the power plant dust conveying stability index, the power plant dust conveying stability index, the power plant dust conveying adhesion index, and the power plant ash conveying pipeline dust density value respectively.

2. The power plant ash handling control optimization system according to claim 1 is characterized in that: The power plant ash transportation status data includes the pipeline surface roughness value, pipeline diameter value, conveying gas secondary flow value, conveying gas viscosity value, conveying gas flow rate value, and conveying dust density value of the power plant ash transportation pipeline.

3. The power plant ash handling control optimization system according to claim 2 is characterized in that: The specific steps to obtain the power plant conveying dust stability index are as follows: Obtain the dynamic shape factors of dust transported in the ash transport pipeline of the power plant; The dust adhesion index of the power plant is read, and a comprehensive analysis is performed based on the secondary flow value of the conveying gas in the power plant's ash conveying pipeline, the conveying gas velocity value, the conveying dust density value, and the dynamic morphological factor of the conveying dust in the power plant's ash conveying pipeline to obtain the power plant's dust stability index.

4. The power plant ash handling control optimization system according to claim 3 is characterized in that: The specific formula for calculating the dust stability index of a power plant is as follows: Among them, HwD is the dust transport stability index of the power plant, XtY is the dynamic morphological factor of the dust transported in the ash transport pipeline of the power plant, QtL, EcL, and HcM are the transport gas flow rate value, the transport gas secondary flow value, and the transport dust density value of the ash transport pipeline of the power plant respectively, and NfZ is the dust transport adhesion index of the power plant.

5. The power plant ash handling control optimization system according to claim 2, characterized in that: The specific steps to obtain the power plant flow field efficiency index are as follows: Read the gas viscosity and dust density of the power plant ash pipeline, and analyze the kinematic viscosity of the gas in the power plant ash pipeline; The dust adhesion index of the power plant is read, and a comprehensive analysis is performed based on the conveying gas velocity value, conveying dust density value, and conveying gas kinematic viscosity value of the power plant ash conveying pipeline to obtain the power plant flow field efficiency index.

6. The power plant ash handling control optimization system according to claim 5, characterized in that: The specific formula for calculating the power plant flow field efficiency index is as follows: Among them, LcX is the flow field efficiency index of the power plant, QtL is the conveying gas flow rate value of the power plant ash conveying pipeline, QyN is the conveying gas kinematic viscosity value of the power plant ash conveying pipeline, ξ is the flow rate viscosity influence coefficient stored in the database, NfZ is the power plant conveying dust adhesion index, and HcM is the conveying dust density value of the power plant ash conveying pipeline.

7. The power plant ash handling control optimization system according to claim 2, characterized in that: The specific steps to obtain the dust adhesion index of power plant transportation are as follows: Read the surface roughness and diameter of the power plant ash transport pipeline, and analyze the adhesion influencing factors of the power plant ash transport pipeline; The adhesion influencing factors of the power plant ash conveying pipeline are combined with the secondary flow rate value and the viscosity value of the conveying gas in the power plant ash conveying pipeline to obtain the power plant dust adhesion index.

8. The power plant ash handling control optimization system according to claim 7, characterized in that: The specific formulas for calculating the adhesion influencing factor of the power plant ash pipeline and the dust adhesion index of the power plant are as follows: Among them, GnY is the adhesion influencing factor of the ash conveying pipeline of the power plant, GbC is the surface roughness value of the ash conveying pipeline of the power plant, η1 and δ1 are the pipeline surface roughness influence coefficient and pipeline surface roughness weight coefficient stored in the database respectively, GzJ is the pipeline diameter value of the ash conveying pipeline of the power plant, η2 and δ2 are the pipeline diameter influence coefficient and pipeline diameter weight coefficient stored in the database respectively, δ1+δ2=1, NfZ is the dust adhesion index of the power plant, EcL and NdZ are the secondary flow value and the viscosity value of the conveying gas of the ash conveying pipeline of the power plant respectively.

9. The power plant ash handling control optimization system according to claim 2, characterized in that: The specific steps for analyzing the ash transport adjustment flow rate value and ash transport adjustment pressure value of the transport gas in the power plant ash transport pipeline are as follows: Read the power plant ash conveying efficiency correction index, and conduct a comprehensive analysis based on the power plant dust adhesion index, the dust density value of the power plant ash conveying pipeline, the secondary flow value of the conveying gas in the power plant ash conveying pipeline, and the conveying gas viscosity value to obtain the ash conveying adjustment flow rate value of the conveying gas in the power plant ash conveying pipeline; The viscosity value of the conveying gas in the power plant ash conveying pipeline is comprehensively analyzed in combination with the power plant ash conveying efficiency correction index and the ash conveying adjustment flow rate value of the conveying gas in the power plant ash conveying pipeline to obtain the ash conveying adjustment pressure value of the conveying gas in the power plant ash conveying pipeline.

10. The power plant ash handling control optimization system according to claim 9, characterized in that: The specific formula for calculating the ash delivery adjustment flow rate value and ash delivery adjustment pressure value of the transport gas in the power plant ash delivery pipeline is as follows: Among them, TsZ is the ash transport adjustment flow rate value of the conveying gas in the ash conveying pipeline of the power plant, ShX is the ash transport efficiency correction index of the power plant, NfZ is the dust adhesion index of the power plant, HcM, EcL, and NdZ are the dust density value, secondary flow value, and viscosity value of the conveying gas in the ash conveying pipeline of the power plant respectively, and TyZ is the ash transport adjustment pressure value of the conveying gas in the ash conveying pipeline of the power plant.

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