Soot blowing test method and system for sound wave generator

Abstract

The invention provides a sound wave generator soot blowing test method and system. The method comprises the steps that an experiment platform for simulating the working environment of a low-low-temperature economizer is built, and a test sample and sensing equipment are installed; setting power output parameters including a fixed power level and pulse power modulation parameter, a soot blowing frequency and phase control parameter, a time period and adaptive adjustment parameter, a sound wave frequency and an airflow velocity and vector control parameter; soot blowing operation is carried out, data are collected through sensing equipment, the soot coverage rate and particle information are obtained through an optical imaging sensor, and the pressure intensity generated by sound waves at the position where soot particles are located is obtained through an acoustic sensor array; calculating cleaning efficiency, and performing adjustment and iterative experiment on parameters according to the cleaning efficiency and data feedback; comprehensive optimization of the soot blowing process of the sound wave generator is achieved, the soot blowing effect of the low-low-temperature economizer is improved, then the overall operation efficiency of energy equipment is improved, the service life of the equipment is prolonged, and the requirements for environmental protection and energy saving are met.

Description

Technical Field

[0001] The invention relates to the technical field of equipment maintenance, and in particular to a soot blowing test method and system of an acoustic wave generator. Background Art

[0002] In the field of energy equipment, low-temperature economizer is an important component, and its main function is to recover flue gas waste heat and improve energy utilization efficiency. However, in actual operation, the heat exchange components of low-temperature economizer are prone to dust accumulation. With the increasingly stringent environmental protection requirements, higher requirements are placed on the efficient operation of energy equipment. Dust accumulation will increase the resistance of low-temperature economizer, making flue gas circulation unsmooth and increasing fan energy consumption. At the same time, dust accumulation will form an insulation layer on the surface of the heat exchange component, seriously reducing the heat exchange efficiency and affecting the energy recovery effect, which is contrary to the general trend of environmental protection and energy saving.

[0003] In the early days, manual cleaning was a common method, and workers regularly cleaned the dust on the surface of the heat exchange element manually. This method is labor-intensive, inefficient, and easily causes damage to the equipment during the cleaning process. At the same time, it cannot meet the needs of continuous operation of large-scale energy equipment. The emergence of sonic soot blowing technology has brought new ideas for solving the dust accumulation problem. Sonic soot blowing uses high-intensity sound waves generated by a sonic generator to vibrate the dust particles. When the vibration reaches a certain level, the dust particles overcome the adhesion to the surface of the heat exchange element and detach. Sonic soot blowing has the advantages of non-contact and no thermal stress, will not cause physical damage to the heat exchange element, and has relatively low energy consumption. However, the existing sonic soot blowing technology also has many problems:

[0004] Most existing sonic generators have fixed power output and sonic frequency or a limited adjustable range. In practical applications, different types of soot (such as particle size, composition, and viscosity) and different heat exchange element structures and working conditions have different requirements for soot power and frequency. A single power and frequency setting cannot achieve the best soot blowing effect for various situations, resulting in incomplete soot removal in many cases.

[0005] Current sonic sootblowing technologies often only focus on the parameters of the sonic waves themselves, while ignoring the synergistic effects with other factors. For example, airflow can assist in removing the blown-off soot during the sootblowing process to avoid secondary deposition, but existing technologies rarely jointly optimize the airflow velocity, direction, and sonic wave parameters. At the same time, factors such as the sootblowing time period, frequency, and phase control between multiple sonic wave generators have not been fully considered, and the overall sootblowing effect cannot be maximized.

[0006] For the evaluation of sootblowing effect, the existing technology mainly relies on simple observation or some rough indicators, such as the approximate change of heat exchange efficiency. It lacks accurate and comprehensive data collection and analysis methods, and cannot deeply understand the microscopic processes such as the force and movement of soot particles during sootblowing. It is difficult to accurately determine the specific reasons for the poor sootblowing effect, and thus it is impossible to adjust the sootblowing parameters in a targeted manner, which limits the further optimization of sootblowing technology.

[0007] Therefore, there is an urgent need in the art for a soot blowing test method and system using an acoustic wave generator to solve the above problems. Summary of the invention

[0008] The present invention provides a sootblowing test method and system for an acoustic wave generator, aiming to solve the problems in the above-mentioned prior art, realize comprehensive optimization of the sootblowing process of the acoustic wave generator, improve the sootblowing effect of the low-temperature economizer, thereby improving the overall operating efficiency of the energy equipment, extending the service life of the equipment, and meeting the requirements of environmental protection and energy saving.

[0009] In one aspect, the present invention provides a soot blowing test method using an acoustic wave generator, comprising:

[0010] Step 1: build an experimental platform simulating the working environment of a low-temperature economizer and install test samples and sensor equipment;

[0011] Step 2, setting power output parameters, including fixed power level and pulse power modulation parameters, soot blowing frequency and phase control parameters, time period and adaptive adjustment parameters, sound wave frequency, and air flow velocity and vector control parameters;

[0012] Step 3: Perform soot blowing operations and collect data through sensor equipment, including obtaining soot coverage and particle information through optical imaging sensors, and obtaining the pressure generated by sound waves at the locations of soot particles through acoustic sensor arrays;

[0013] Step 4: Calculate the cleaning efficiency based on the collected data, and adjust the parameters and iterate the experiment according to the cleaning efficiency and data feedback.

[0014] According to a sonic wave generator soot blowing test method provided by the present invention, in step 1, the size of the experimental platform and the standard values ​​of temperature, humidity and pressure are preset;

[0015] A flat-plate test sample is placed in the experimental platform, and its material is the same metal material as the actual economizer;

[0016] The experimental platform is equipped with sensing equipment including a high-resolution optical imaging sensor and an acoustic sensor array.

[0017] According to a soot blowing test method of an acoustic wave generator provided by the present invention, in step 2, the fixed power level and pulse power modulation parameters include:

[0018] The fixed power level is preset; the pulse power modulation has a preset pulse width and pulse interval;

[0019] Preset the sound wave frequency and calculate the sound wave angular frequency ω;

[0020] Preset initial phase

[0021] The sound wave generator generates a certain sound pressure amplitude at a specific distance from the sample. Based on the relationship between the sound wave generator structure and the sample position, the coefficient A is determined by finite element simulation method. s , coefficient A s Used to reflect the spatial characteristics and energy coupling relationship of sound wave propagation;

[0022] For dust particles, which have specific physical properties of size, shape, and material density, the coefficient k is determined by combining theory and experiment based on Mie acoustic theory and acoustic resonance related experiments. s,i , coefficient k s,i It is used to reflect the absorption and response characteristics of the physical properties of dust particles to sound wave energy under the action of sound waves.

[0023] According to a soot blowing test method provided by the present invention, in step 2, the soot blowing frequency and phase control parameters include:

[0024] The preset initial soot blowing frequency, when multiple simulated sound wave generators work simultaneously, the preset initial quantity and phase difference are preset.

[0025] According to a soot blowing test method provided by the present invention, the time period and the adaptive adjustment parameters include:

[0026] The initial soot blowing time cycle is preset, and the soot accumulation thickness threshold is set to 0.1 mm. When the optical imaging sensor detects that the soot accumulation thickness exceeds this threshold, the next soot blowing time interval is shortened by the preset value.

[0027] According to a soot blowing test method provided by the present invention, the air flow velocity and vector control parameters include:

[0028] Preset air flow velocity v g (t) and the angle θ between the airflow direction and the test sample surface;

[0029] Based on the structural characteristics of the airflow generator and the flow field inside the platform, the airflow density ρ is calculated using fluid mechanics software. g ;

[0030] Calculate the projection area A of the dust particles in the airflow direction g,i =πr 2 cosθ; where r is the average radius of the dust particles, which is determined by the optical imaging sensor detection in step 3;

[0031] Unit vector that determines the direction of airflow

[0032] Calculation of drag coefficient C based on particle shape and Reynolds number of dust particles d .

[0033] According to a soot blowing test method provided by the present invention, in step three, the process of performing the soot blowing operation and collecting data through the sensor device includes:

[0034] Before soot blowing begins, an image of dust accumulation on the surface of the test sample is obtained by an optical imaging sensor;

[0035] The initial dust coverage G, the average radius of dust particles r, and the dust density ρ are obtained by image analysis algorithm. h ;

[0036] Calculate the total mass of initial dust accumulation Wherein x is the area of ​​the flat test sample;

[0037] The pressure P(N) generated by the sound wave at the location of the dust particles at each moment is obtained through the acoustic sensor array.

[0038] According to a soot blowing test method provided by the present invention, in step 4, the calculation process of calculating the cleaning efficiency based on the collected data includes:

[0039] S41, calculate the acoustic force F received by the i-th dust particle at time t s,i(t) , the calculation formula is:

[0040]

[0041] Wherein, P(t) is the acoustic wave pressure at time t, which is determined based on the detection result P(N) of the acoustic sensor array; the acoustic wave force F s,i(t) Used to reflect the force exerted by sound waves on dust particles during propagation;

[0042] S42, calculate the airflow force F on the i-th dust particle at time t g,i(t) , the calculation formula is:

[0043]

[0044] The air flow force F g,i(t)Used to reflect the force of airflow on the dust particles during the soot blowing process;

[0045] S43, calculate the adhesion force F of the i-th dust particle at time t a,i(t) , the calculation formula is:

[0046] F a,i(t) =F v,i +F e,i +F m,i

[0047] Among them, F v,i is the van der Waals force calculated based on the Lifshitz-van der Waals theory, F e,i To obtain the electrostatic force based on the charge distribution on the dust particles and the heat exchange element surface, F m,i The capillary force is calculated from the surface tension coefficient of the liquid and the contact angle, both of which are determined experimentally before or after soot blowing; the adhesion force F a,i(t) Used to reflect the force that prevents dust particles from detaching from the surface of the heat exchange element;

[0048] S43, calculate the inter-particle interaction force F on the i-th dust particle at time t i,i(t) , the calculation formula is:

[0049]

[0050] Where n is the total number of dust particles; van der Waals force F v,ij , electrostatic force F e,ij With F v,i 、F e,i The determination process of F is similar. c,ij is the collision force determined based on the Hertzian contact theory, which is determined experimentally before or after soot blowing; the particle interaction force F i,i(t) Used to reflect the interaction between dust particles;

[0051] S44, determining the cleaning efficiency E; the calculation formula is:

[0052]

[0053] Among them, t 1 ,t 2 are the start and stop time of soot blowing, respectively, and are the velocity of the ith ash particle at time t, which are obtained by solving the motion equation of the ash particles based on Newton's second law.

[0054] According to a soot blowing test method provided by the present invention, the process of adjusting parameters and iterating experiments according to cleaning efficiency and data feedback includes:

[0055] After the soot blowing experiment is completed, analysis is performed based on the cleaning efficiency results and the data collected by the sensor; if the cleaning efficiency E does not reach the expected target, that is, the expected target is that the cleaning efficiency E is greater than the preset threshold, the fixed power level and pulse power modulation parameters, soot blowing frequency and phase control parameters, time period and adaptive adjustment parameters, air flow speed and vector control parameters are adjusted accordingly according to the data feedback;

[0056] Repeat steps 1 to 4 for the next round of experiments, and continuously optimize the parameters to improve the cleaning efficiency.

[0057] In another aspect, the present invention provides a sonic wave generator soot blowing test system, comprising:

[0058] The experimental platform building module is used to construct a space that simulates the working environment of the low-temperature economizer, provide an installation basis for test samples and sensor equipment, and control the temperature, humidity and pressure parameters of the experimental environment to meet the experimental requirements;

[0059] A parameter setting module, which is connected to the experimental platform building module and is used to pre-set various initial key parameters in the soot blowing test, including fixed power level and pulse power modulation parameters, soot blowing frequency and phase control parameters, time period and adaptive adjustment parameters, sound wave frequency, and airflow velocity and vector control parameters;

[0060] A soot blowing execution module, which is connected to the experimental platform building module and the parameter setting module, and is used to perform soot blowing operations according to preset parameters, including a sound wave generator generating sound waves to act on the dust particles, and an air flow generator generating an air flow with a specific speed and direction to assist in soot blowing and take away the dust;

[0061] A data collection module, which is connected to the experimental platform building module and is used to obtain various information during the soot blowing process through a sensor device, including the soot coverage rate, particle information, and the pressure generated by the sound wave at the location of the soot particles;

[0062] A data analysis module, connected to the parameter setting module and the data collection module, for calculating the acoustic force, airflow force, adhesion force, and interaction force of the dust particles according to preset parameters, and calculating the cleaning efficiency in combination with the information obtained by the data collection module;

[0063] The experimental optimization module is connected to the data analysis module and the parameter setting module, and is used to determine whether the expected goal is achieved based on the cleaning efficiency obtained. If not, the various preset parameters in the parameter setting module are adjusted according to the data feedback to determine the parameter settings for the next round of experiments, thereby realizing iterative optimization of the experiment to improve the cleaning efficiency.

[0064] Compared with the prior art, the beneficial effects of this application are:

[0065] This application comprehensively considers various sootblowing parameters, such as fixed power level and pulse power modulation parameters, sootblowing frequency and phase control parameters, time cycle and adaptive adjustment parameters, sound wave frequency, airflow velocity and vector control parameters, etc. By accurately setting and adjusting these parameters, more accurate sootblowing operation can be achieved for different types of soot deposits (such as soot deposits of different particle sizes, compositions, and viscosities) and different heat exchange element structures and working conditions.

[0066] This application accurately calculates the cleaning efficiency by considering the microscopic processes such as the force and movement of the dust particles during the soot blowing process, so that researchers can accurately evaluate the soot blowing effect and identify the specific reasons for the poor soot blowing effect. For example, the incomplete removal of dust in a certain area is due to insufficient sonic force, inappropriate air flow velocity, or the influence of the interaction force between particles.

[0067] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0068] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0070] Figure 1 It is a flow chart of a soot blowing test method using an acoustic wave generator provided in an embodiment of the present invention;

[0071] Figure 2 It is a structural schematic diagram of a sonic wave generator soot blowing test system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0073] Embodiment 1:

[0074] The present invention provides a soot blowing test method using an acoustic wave generator. Figure 1 ,include:

[0075] Step 1: build an experimental platform simulating the working environment of a low-temperature economizer and install test samples and sensor equipment;

[0076] Step 2, setting power output parameters, including fixed power level and pulse power modulation parameters, soot blowing frequency and phase control parameters, time period and adaptive adjustment parameters, sound wave frequency, and air flow velocity and vector control parameters;

[0077] Step 3: Perform soot blowing operations and collect data through sensor equipment, including obtaining soot coverage and particle information through optical imaging sensors, and obtaining the pressure generated by sound waves at the locations of soot particles through acoustic sensor arrays;

[0078] Step 4: Calculate the cleaning efficiency based on the collected data, and adjust the parameters and iterate the experiment according to the cleaning efficiency and data feedback.

[0079] The principle and beneficial effect of this embodiment are as follows: sound waves are mechanical waves. When they act on the dust accumulation surface, they can produce a vibration effect, which weakens the adhesion between particles in the dust accumulation layer and between particles and the substrate, thereby facilitating the removal of dust accumulation; by adjusting parameters such as the power output, frequency, blowing frequency, and phase control of the sound waves, the most effective removal strategy can be adopted for different types of dust accumulation (such as loose type and adhesive type); optical imaging sensors and acoustic sensor arrays are used to monitor the dust accumulation status and removal effect in real time, and the parameters are automatically adjusted according to the actual cleaning efficiency to achieve efficient and accurate dust removal;

[0080] Compared with traditional manual or mechanical cleaning methods, the use of ultrasonic technology can more thoroughly remove dust from hard-to-reach areas, reduce energy consumption, and improve equipment operating efficiency. This method is suitable for dust removal tasks in a variety of industrial scenarios, and is easy to adjust parameter settings according to different application requirements, with good applicability and flexibility.

[0081] In order to further optimize the above embodiment, in step 1, the size of the experimental platform and the standard values ​​of temperature, humidity and pressure are preset;

[0082] A flat test sample is placed in the test platform, and its material is the same metal as the actual economizer;

[0083] The experimental platform is equipped with sensing equipment, including high-resolution optical imaging sensors and acoustic sensor arrays;

[0084] In step 2, the fixed power level and pulse power modulation parameters include:

[0085] Fixed power levels are pre-set; pulse power modulation has preset pulse width and pulse interval;

[0086] Preset the sound wave frequency and calculate the sound wave angular frequency ω;

[0087] Preset initial phase

[0088] The sound wave generator generates a certain sound pressure amplitude at a specific distance from the sample. Based on the relationship between the sound wave generator structure and the sample position, the coefficient A is determined by finite element simulation method. s , coefficient A s Used to reflect the spatial characteristics and energy coupling relationship of sound wave propagation;

[0089] For dust particles, which have specific physical properties of size, shape, and material density, the coefficient k is determined by combining theory and experiment based on Mie acoustic theory and acoustic resonance related experiments. s,i , coefficient k s,i It is used to reflect the absorption and response characteristics of the physical properties of dust particles to sound wave energy under the action of sound waves.

[0090] It should be noted that the coefficient A s It mainly reflects the combined influence of the structure of the sound wave generator and the position of the sample on the propagation of the sound wave energy. It links the output of the sound wave generator (such as the sound pressure amplitude) with the effective sound wave energy acting on the dust particles. In terms of physical essence, it takes into account the attenuation, reflection, refraction and other factors of the sound wave in the process of propagating from the generator to the location of the dust particles. If the sound wave generator is a horn-shaped device, the size and shape of its opening, as well as the distance and relative angle to the sample, will affect the energy intensity of the sound wave at the sample position. Coefficient A s The larger the value, the stronger the effective sound wave energy reaching the dust particle position under the same sound wave generator output power, and the more effectively it can drive the dust particles to vibrate. Finite element software (such as ANSYS, COMSOL, etc.) can be used to model the space where the sound wave generator and the sample are located. The structural parameters of the sound wave generator (such as size, material, vibration mode, etc.) and the location information of the sample (distance, angle, etc.) are used as input conditions. By solving the sound wave propagation equation in this space, the relationship between the sound pressure distribution at different positions and the sound wave generator parameters is obtained, thereby determining the coefficient A. s ;

[0091] Coefficient k s,i It is closely related to the physical properties of the dust particles themselves, including particle size, shape, material density, elastic modulus, etc. It characterizes the coupling efficiency of dust particles under the action of sound waves, that is, the ability of particles to absorb and respond to sound wave energy. For smaller dust particles, their mass is small and they are more easily driven to vibrate under the same sound wave, and they may have a larger k s,i value; however, due to their greater inertia, larger particles have a relatively weaker response to sound waves, k s,i The value may be smaller. In addition, the material properties of the particles will also affect ks,i For example, particles made of materials with good elasticity can better produce elastic deformation and absorb energy under the action of sound waves, and their k s,i The value will be different from that of particles made of rigid materials. Theoretically, for spherical dust particles, the scattering and absorption characteristics of sound waves can be calculated based on Mie theory, thereby obtaining coefficients related to particle size, material density, sound wave frequency, etc. However, the actual dust particles are complex in shape and need to be corrected in combination with experiments. For example, through an acoustic resonance experiment, the dust particles are placed in a sound wave field of known frequency and intensity, the resonant frequency and response amplitude of the particles are measured, and compared with the theoretical calculation results, the coefficients are corrected to obtain k. s,i .

[0092] In one embodiment, in the step of setting the power output parameters, the fixed power output level is set to 50%, the pulse width is set to 20ms during pulse power modulation, the pulse interval is set to 100ms, and the sound wave frequency is selected to be 500Hz, then the sound wave angular frequency ω=2π*500Hz; the initial phase

[0093] In order to further optimize the above embodiment, in step 2, the soot blowing frequency and phase control parameters include:

[0094] The preset initial soot blowing frequency, when multiple simulated sound wave generators work simultaneously, the preset initial quantity and phase difference are preset.

[0095] It should be noted that, in one embodiment, in the step of setting the soot blowing frequency and phase control parameters, the soot blowing frequency is set to 3 times per minute, and when multiple simulated sound wave generators work simultaneously, the number is set to 3 and the phase difference is set to 30°.

[0096] In order to further optimize the above embodiment, the time period and the adaptive adjustment parameters include:

[0097] The initial soot blowing time cycle is preset, and the soot accumulation thickness threshold is set to 0.1 mm. When the optical imaging sensor detects that the soot accumulation thickness exceeds this threshold, the next soot blowing time interval is shortened by the preset value.

[0098] It should be noted that, in one embodiment, in the steps of setting the time period and adaptively adjusting parameters, the initial soot blowing time period is set to 10 minutes, and the dust accumulation thickness threshold is set to 0.1 mm. When the optical imaging sensor detects that the dust accumulation thickness exceeds this threshold, the next soot blowing time interval is shortened to 5 minutes.

[0099] In order to further optimize the above embodiment, the air flow speed and vector control parameters include:

[0100] Preset air flow velocity vg (t) and the angle θ between the airflow direction and the test sample surface;

[0101] Based on the structural characteristics of the airflow generator and the flow field inside the platform, the airflow density ρ is calculated using fluid mechanics software. g ;

[0102] Calculate the projection area A of the dust particles in the airflow direction g,i =πr 2 cosθ; where r is the average radius of the dust particles, which is determined by the optical imaging sensor detection in step 3;

[0103] Unit vector that determines the direction of airflow

[0104] Calculation of drag coefficient C based on particle shape and Reynolds number of dust particles d .

[0105] It should be noted that the optical imaging sensor obtains the principle of the average radius of dust particles and the resistance coefficient C d The calculation principles are all existing technical means, so they will not be elaborated in detail.

[0106] In order to further optimize the above embodiment, in step 3, the process of performing the soot blowing operation and collecting data through the sensor device includes:

[0107] Before soot blowing begins, an image of dust accumulation on the surface of the test sample is obtained by an optical imaging sensor;

[0108] The initial dust coverage G, the average radius of dust particles r, and the dust density ρ are obtained by image analysis algorithm. h ;

[0109] Calculate the total mass of initial dust accumulation Where x is the area of ​​the flat test sample;

[0110] The pressure P(N) generated by the sound wave at the location of the dust particles at each moment is obtained through the acoustic sensor array.

[0111] It should be noted that the image analysis algorithm is an existing technical means and will not be elaborated on; the area of ​​the flat test sample is pre-set in step one.

[0112] To further optimize the above embodiment, in step 4, the calculation process of calculating the cleaning efficiency based on the collected data includes:

[0113] S41, calculate the acoustic force F received by the i-th dust particle at time t s,i(t) , the calculation formula is:

[0114]

[0115] Where P(t) is the acoustic pressure at time t, which is determined based on the detection result P(N) of the acoustic sensor array; the acoustic force F s,i(t) Used to reflect the force exerted by sound waves on dust particles during propagation;

[0116] S42, calculate the airflow force F on the i-th dust particle at time t g,i(t) , the calculation formula is:

[0117]

[0118] Air flow force F g,i(t) Used to reflect the force of airflow on the dust particles during the soot blowing process;

[0119] S43, calculate the adhesion force F of the i-th dust particle at time t a,i(t) , the calculation formula is:

[0120] F a,i(t) =F v,i +F e,i +F m,i

[0121] Among them, F v,i is the van der Waals force calculated based on the Lifshitz-van der Waals theory, F e,i To obtain the electrostatic force based on the charge distribution on the dust particles and the heat exchange element surface, F m,i The capillary force is calculated from the surface tension coefficient of the liquid and the contact angle, both of which are determined experimentally before or after soot blowing; the adhesion force F a,i(t) Used to reflect the force that prevents dust particles from detaching from the surface of the heat exchange element;

[0122] S43, calculate the inter-particle interaction force F on the i-th dust particle at time t i,i(t) , the calculation formula is:

[0123]

[0124] Where n is the total number of dust particles; van der Waals force F v,ij , electrostatic force F e,ij With F v,i 、F e,i The determination process of F is similar. c,ij is the collision force determined based on the Hertzian contact theory, which is determined experimentally before or after soot blowing; the particle interaction force F i,i(t) Used to reflect the interaction between dust particles;

[0125] S44, determining the cleaning efficiency E; the calculation formula is:

[0126]

[0127] Among them, t 1 ,t 2 are the start and stop time of soot blowing, respectively, and are the velocity of the ith ash particle at time t, which are obtained by solving the motion equation of the ash particles based on Newton's second law.

[0128] It should be noted that for the adhesion force F a,i(t) Parameters in the van der Waals force F v,i It can be calculated by their material properties (such as dielectric constant, refractive index, etc.) and geometric shapes. For the case of spherical dust particles and the surface of a flat heat exchange element, the van der Waals force formula is: Where A is the Hamaker constant, which depends on the properties of the particles and surface materials and can be obtained by consulting relevant material manuals or experimental measurements; d is the distance between the particle and the surface, which can be approximated to the atomic distance (about a few angstroms) when in close contact; another method is to use an atomic force microscope (AFM) to directly measure the van der Waals force. The AFM probe simulates the dust particles, and by measuring the force-distance curve between the probe and the sample surface (simulating the surface of the heat exchange element), when approaching the surface, when the distance is small enough, the measured force is mainly the van der Waals force. By measuring samples of different particle sizes and materials multiple times, the relationship between the van der Waals force and parameters such as particle size is fitted.

[0129] Electrostatic force F e,i The calculation is based on Coulomb's law. First, it is necessary to determine the charge distribution of the dust particles and the surface of the heat exchange element. If it is assumed that the particles and the surface have a uniform charge Q 1 and Q 2 , then the electrostatic force formula is Where k is the Coulomb constant and r is the distance between the center of the particle and the center of the surface charge; however, in actual situations, the charge distribution is usually uneven, and it is necessary to solve the Poisson equation of the electrostatic field to obtain a more accurate electrostatic force. This involves the analysis of the conductivity, dielectric constant, and charge generation mechanism (such as friction charging, electrostatic induction, etc.) of the particles and surface materials. Another method is to use an electrometer to measure the potential difference between the dust particles and the surface of the heat exchange element. By changing the environmental conditions (such as humidity, friction, etc.) to observe the change in potential difference, the generation and distribution of charge can be inferred. Then, the electrostatic force is calculated based on the theoretical model and parameters such as the measured potential difference. In addition, an electric field force microscope (EFM) can also be used to directly measure the distribution of electrostatic forces between particles and surfaces.

[0130] Capillary force F m,iThe calculation is based on surface tension theory. When there is a trace liquid film (such as water) between the dust particles and the surface of the heat exchange element, the capillary force can be calculated according to the Young-Laplace equation. For a cylindrical liquid meniscus (a common simplified model), the capillary force formula is F m,i =2πrγcosδ, where γ is the surface tension coefficient of the liquid, which can be obtained by consulting the physical properties table of the liquid; δ is the contact angle between the liquid and the particle or surface, which is measured by a contact angle meter; r is the radius of the dust particle; Another method is to measure the contact angle by observing the shape of the liquid between the particle and the surface. Drop a small amount of liquid on a simulated system of particles and surfaces, and use an optical microscope or a video contact angle measurement system to record the shape of the liquid to determine the contact angle. Then, the capillary force is calculated by combining the surface tension coefficient of the liquid and the particle radius.

[0131] For the interaction force F i,i(t) The parameters in the equation are as follows: the van der Waals force and electrostatic force are determined in the same way as the adhesion force, so they will not be further explained; the collision force F c,ij According to the Hertzian contact theory, the collision force between two elastic spheres (dust particles) is given by Where E * is the equivalent elastic modulus, which is related to the elastic modulus of the granular material and is obtained through material mechanical properties testing; is the equivalent radius. For two particles with radii R1 and R2, δ is the contact deformation when particles collide, which can be calculated by combining the kinematic equation with the parameters such as the particle's velocity, mass and elastic recovery coefficient. Another method is to record the collision process between dust particles with a high-speed camera and measure the velocity and position change of the particles before and after the collision. The collision force is calculated based on the law of conservation of momentum and energy, combined with the material properties and geometric dimensions of the particles. A micro-force sensor can also be used to directly measure the force generated when particles collide.

[0132] In order to further optimize the above embodiment, the process of adjusting parameters and iterative experiments based on cleaning efficiency and data feedback includes:

[0133] After the soot blowing experiment is completed, analysis is performed based on the cleaning efficiency results and the data collected by the sensor; if the cleaning efficiency E does not reach the expected target, that is, the expected target is that the cleaning efficiency E is greater than the preset threshold, the fixed power level and pulse power modulation parameters, soot blowing frequency and phase control parameters, time period and adaptive adjustment parameters, air flow speed and vector control parameters are adjusted accordingly according to the data feedback;

[0134] Repeat steps 1 to 4 for the next round of experiments, and continuously optimize the parameters to improve the cleaning efficiency.

[0135] It should be noted that after the soot blowing experiment, an analysis is performed based on the cleaning efficiency results and the data collected by the sensor. If the cleaning efficiency does not reach the expected target (assuming the expected target is a cleaning efficiency greater than 80%), the parameters are adjusted based on the data feedback. For example, if it is found that the dust removal effect in a certain area is poor, it may be that the airflow vector in the area is unreasonable. The airflow direction can be adjusted or the local airflow speed can be increased; or if it is found that certain frequencies of sound waves have no obvious effect on dust removal, the sound wave frequency combination can be adjusted. Then repeat the above steps for the next round of experiments, and continuously optimize the parameters to improve the cleaning efficiency.

[0136] Embodiment 2:

[0137] The present invention provides a soot blowing test system using an acoustic wave generator. Figure 2 ,include:

[0138] The experimental platform building module is used to construct a space that simulates the working environment of the low-temperature economizer, provide an installation basis for test samples and sensor equipment, and control the temperature, humidity and pressure parameters of the experimental environment to meet the experimental requirements;

[0139] A parameter setting module, which is connected to the experimental platform building module and is used to pre-set various initial key parameters in the soot blowing test, including fixed power level and pulse power modulation parameters, soot blowing frequency and phase control parameters, time period and adaptive adjustment parameters, sound wave frequency, and air flow velocity and vector control parameters;

[0140] A soot blowing execution module, which is connected to the experimental platform building module and the parameter setting module, is used to perform soot blowing operations according to preset parameters, including a sound wave generator generating sound waves to act on the soot particles, and an air flow generator generating an air flow with a specific speed and direction to assist soot blowing and take away the soot;

[0141] A data collection module, which is connected to the experimental platform building module and is used to obtain various information during the soot blowing process through sensing equipment, including the soot coverage rate, particle information, and the pressure generated by the sound wave at the location of the soot particles;

[0142] A data analysis module, which is connected to the parameter setting module and the data collection module, and is used to calculate the acoustic force, airflow force, adhesion force, and interaction force of the dust particles according to preset parameters, and calculate the cleaning efficiency in combination with the information obtained by the data collection module;

[0143] The experiment optimization module is connected with the data analysis module and the parameter setting module, and is used to judge whether the expected goal is achieved based on the cleaning efficiency obtained. If not, the various preset parameters in the parameter setting module are adjusted according to the data feedback to determine the parameter settings for the next round of experiments, thereby realizing iterative optimization of the experiment and improving the cleaning efficiency.

[0144] The workflow of this embodiment is as follows: after the experimental platform building module builds the experimental platform and sets the environmental parameters, the parameter setting module sets various soot blowing parameters according to the experimental requirements;

[0145] The soot blowing execution module performs soot blowing operations according to the set parameters, while the data collection module collects various data in the soot blowing process in real time;

[0146] The data collection module transmits the data to the data processing and analysis module, which calculates the cleaning efficiency and performs data analysis;

[0147] The experimental optimization module determines whether the parameters need to be optimized based on the results of the data processing and analysis module. If necessary, the parameters in the parameter setting module are adjusted, and then the soot blowing operation is performed again. This cycle is repeated to continuously optimize the soot blowing effect.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A soot blowing test method using an acoustic wave generator, characterized in that: include: Step 1: build an experimental platform simulating the working environment of a low-temperature economizer and install test samples and sensor equipment; Step 2, setting power output parameters, including fixed power level and pulse power modulation parameters, soot blowing frequency and phase control parameters, time period and adaptive adjustment parameters, sound wave frequency, and air flow velocity and vector control parameters; Step 3: Perform soot blowing operations and collect data through sensor equipment, including obtaining soot coverage and particle information through optical imaging sensors, and obtaining the pressure generated by sound waves at the locations of soot particles through acoustic sensor arrays; Step 4: Calculate the cleaning efficiency based on the collected data, and adjust the parameters and iterate the experiment according to the cleaning efficiency and data feedback.

2. A soot blowing test method using an acoustic wave generator according to claim 1, characterized in that: In step 1, the size of the experimental platform and standard values ​​of temperature, humidity and pressure are preset; A flat-plate test sample is placed in the experimental platform, and its material is the same metal material as the actual economizer; The experimental platform is equipped with sensing equipment including a high-resolution optical imaging sensor and an acoustic sensor array.

3. A soot blowing test method using an acoustic wave generator according to claim 2, characterized in that: In step 2, the fixed power level and pulse power modulation parameters include: The fixed power level is preset; the pulse power modulation has a preset pulse width and pulse interval; Preset the sound wave frequency and calculate the sound wave angular frequency ω; Preset initial phase The sound wave generator generates a certain sound pressure amplitude at a specific distance from the sample. Based on the relationship between the sound wave generator structure and the sample position, the coefficient A is determined by finite element simulation method. s , coefficient A s Used to reflect the spatial characteristics and energy coupling relationship of sound wave propagation; For dust particles, which have specific physical properties of size, shape, and material density, the coefficient k is determined by combining theory and experiment based on Mie acoustic theory and acoustic resonance related experiments. s,i , coefficient k s,i It is used to reflect the absorption and response characteristics of the physical properties of dust particles to sound wave energy under the action of sound waves.

4. A soot blowing test method using an acoustic wave generator according to claim 1, characterized in that: In step 2, the soot blowing frequency and phase control parameters include: The preset initial soot blowing frequency, when multiple simulated sound wave generators work simultaneously, the preset initial quantity and phase difference are preset.

5. A soot blowing test method using an acoustic wave generator according to claim 1, characterized in that: The time period and adaptive adjustment parameters include: The initial soot blowing time cycle is preset, and the soot accumulation thickness threshold is set to 0.1 mm. When the optical imaging sensor detects that the soot accumulation thickness exceeds this threshold, the next soot blowing time interval is shortened by the preset value.

6. A soot blowing test method using an acoustic wave generator according to claim 3, characterized in that: The air flow velocity and vector control parameters include: Preset air flow velocity v g (t) and the angle θ between the airflow direction and the test sample surface; Based on the structural characteristics of the airflow generator and the flow field inside the platform, the airflow density ρ is calculated using fluid mechanics software. g ; Calculate the projection area A of the dust particles in the airflow direction g,i =πr 2 cosθ; where r is the average radius of the dust particles, which is determined by the optical imaging sensor detection in step 3; Unit vector that determines the direction of airflow Calculation of drag coefficient C based on particle shape and Reynolds number of dust particles d .

7. A soot blowing test method using an acoustic wave generator according to claim 6, characterized in that: In step three, the process of performing the soot blowing operation and collecting data through the sensor device includes: Before soot blowing begins, an image of dust accumulation on the surface of the test sample is obtained by an optical imaging sensor; The initial dust coverage G, the average radius of dust particles r, and the dust density ρ are obtained by image analysis algorithm. h ; Calculate the total mass of initial dust accumulation Wherein x is the area of ​​the flat test sample; The pressure P(N) generated by the sound wave at the location of the dust particles at each moment is obtained through the acoustic sensor array.

8. A soot blowing test method using an acoustic wave generator according to claim 7, characterized in that: In step 4, the calculation process of calculating the cleaning efficiency based on the collected data includes: S41, calculate the acoustic force F received by the i-th dust particle at time t s,i(t) , the calculation formula is: Wherein, P(t) is the acoustic wave pressure at time t, which is determined based on the detection result P(N) of the acoustic sensor array; the acoustic wave force F s,i(t) Used to reflect the force exerted by sound waves on dust particles during propagation; S42, calculate the airflow force F on the i-th dust particle at time t g,i(t) , the calculation formula is: The air flow force F g,i(t) Used to reflect the force of airflow on the dust particles during the soot blowing process; S43, calculate the adhesion force F of the i-th dust particle at time t a,i(t) , the calculation formula is: F a,i(t) =F v,i +F e,i +F m,i Among them, F v,i is the van der Waals force calculated based on the Lifshitz-van der Waals theory, F e,i To obtain the electrostatic force based on the charge distribution on the dust particles and the heat exchange element surface, F m,i The capillary force is calculated from the surface tension coefficient of the liquid and the contact angle, both of which are determined experimentally before or after soot blowing; the adhesion force F a,i(t) Used to reflect the force that prevents dust particles from detaching from the surface of the heat exchange element; S43, calculate the inter-particle interaction force F on the i-th dust particle at time t i,i(t) , the calculation formula is: Where n is the total number of dust particles; van der Waals force F v,ij , electrostatic force F e,ij With F v,i 、F e,i The determination process of F is similar. c,ij is the collision force determined based on the Hertzian contact theory, which is determined experimentally before or after soot blowing; the particle interaction force F i,i(t) Used to reflect the interaction between dust particles; S44, determining the cleaning efficiency E; the calculation formula is: Among them, t1 and t2 are the start and stop times of soot blowing, respectively, and is the velocity of the i-th ash particle at time t, which is obtained by solving the motion equation of the ash particles based on Newton's second law.

9. A soot blowing test method using an acoustic wave generator according to claim 8, characterized in that: The process of adjusting parameters and iterating experiments based on cleaning efficiency and data feedback includes: After the soot blowing experiment is completed, analysis is performed based on the cleaning efficiency results and the data collected by the sensor; if the cleaning efficiency E does not reach the expected target, that is, the expected target is that the cleaning efficiency E is greater than the preset threshold, the fixed power level and pulse power modulation parameters, soot blowing frequency and phase control parameters, time period and adaptive adjustment parameters, air flow speed and vector control parameters are adjusted accordingly according to the data feedback; Repeat steps 1 to 4 for the next round of experiments, and continuously optimize the parameters to improve the cleaning efficiency.

10. A soot blowing test system using an acoustic wave generator, based on the method described in claims 1 to 9, characterized in that: include: The experimental platform building module is used to construct a space that simulates the working environment of the low-temperature economizer, provide an installation basis for test samples and sensor equipment, and control the temperature, humidity and pressure parameters of the experimental environment to meet the experimental requirements; A parameter setting module, which is connected to the experimental platform building module and is used to pre-set various initial key parameters in the soot blowing test, including fixed power level and pulse power modulation parameters, soot blowing frequency and phase control parameters, time period and adaptive adjustment parameters, sound wave frequency, and airflow velocity and vector control parameters; A soot blowing execution module, which is connected to the experimental platform building module and the parameter setting module, and is used to perform soot blowing operations according to preset parameters, including a sound wave generator generating sound waves to act on the dust particles, and an air flow generator generating an air flow with a specific speed and direction to assist in soot blowing and take away the dust; A data collection module, which is connected to the experimental platform building module and is used to obtain various information during the soot blowing process through a sensor device, including the soot coverage rate, particle information, and the pressure generated by the sound wave at the location of the soot particles; A data analysis module, connected to the parameter setting module and the data collection module, for calculating the acoustic force, airflow force, adhesion force, and interaction force of the dust particles according to preset parameters, and calculating the cleaning efficiency in combination with the information obtained by the data collection module; The experimental optimization module is connected to the data analysis module and the parameter setting module, and is used to determine whether the expected goal is achieved based on the cleaning efficiency obtained. If not, the various preset parameters in the parameter setting module are adjusted according to the data feedback to determine the parameter settings for the next round of experiments, thereby realizing iterative optimization of the experiment to improve the cleaning efficiency.

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