Intelligent sound wave soot blowing method, system and terminal for coal-fired boiler flue

Abstract

The invention relates to an intelligent sound wave soot blowing method, system and terminal for a coal-fired boiler flue, and belongs to the technical field of coal-fired boiler flue cleaning.The intelligent sound wave soot blowing method comprises the steps that environmental parameters in the coal-fired boiler flue are monitored in real time; calculating sound wave parameters according to the environmental parameters; according to the sound wave parameters, a sound wave emitter is controlled to emit needed sound waves to a flue; the ash removal effect of accumulated ash in the flue is monitored; and according to the feedback ash removal effect, sound wave parameters are optimized so that sound wave energy can be evenly distributed in the flue. The flue dust removal device has the beneficial effect that the dust removal efficiency of the flue of the coal-fired boiler is improved.

Description

Technical Field

[0001] This application relates to the technical field of cleaning flue gas ducts of coal-fired boilers, and in particular to an intelligent acoustic soot blowing method, system and terminal for flue gas ducts of coal-fired boilers. Background Technology

[0002] Flue gas ash accumulation is a common and challenging problem during the operation of coal-fired boilers. When solid or liquid fuels burn in the boiler, a large amount of soot adheres to the boiler's heating surfaces and flue gas. Ash accumulation on the heating surfaces reduces boiler thermal efficiency, increases energy consumption, and shortens equipment lifespan. When ash accumulation is severe, not only does heat exchange deteriorate, but the increased flue gas resistance also affects the normal operation of the boiler, potentially forcing a shutdown for ash removal and consequently impacting production. Therefore, timely and effective removal of flue gas ash is crucial for ensuring the efficient and stable operation of the boiler.

[0003] Currently, the industry mostly uses low-frequency acoustic cleaning technology for flue gas cleaning of coal-fired boilers. Acoustic sootblowers are characterized by simple structure, small size, safety and reliability, and can be applied to various furnace types and different parts of boilers. However, acoustic sootblowers mostly use fixed-frequency sound waves for cleaning, and the sound wave energy is unevenly distributed in the flue, which cannot effectively remove accumulated ash, resulting in low cleaning efficiency. Summary of the Invention

[0004] To improve the ash removal efficiency of coal-fired boiler flues, this application provides an intelligent acoustic ash blowing method, system, and terminal for coal-fired boiler flues.

[0005] Firstly, this application provides an intelligent acoustic soot blowing method for flue gas ducts of coal-fired boilers, employing the following technical solution: A smart acoustic soot blowing method for coal-fired boiler flues includes: Real-time monitoring of environmental parameters within the flue of coal-fired boilers; Calculate the acoustic parameters based on the environmental parameters; Based on the sound wave parameters, control the sound wave transmitter to emit the required sound waves into the flue. Monitor the cleaning effect of ash accumulation in the flue; Based on the feedback regarding the dust removal effect, the acoustic parameters were optimized to ensure that the acoustic energy was evenly distributed within the flue.

[0006] By adopting the above technical solution, and calculating and controlling the acoustic parameters by monitoring environmental parameters within the flue in real time, the soot removal becomes more targeted, allowing for precise treatment of accumulated ash and effectively improving cleaning efficiency. Simultaneously, monitoring and optimizing the acoustic parameters ensures that acoustic energy is evenly distributed within the flue, avoiding cleaning dead zones and ensuring that ash is effectively removed from all parts of the flue, guaranteeing the overall cleanliness of the flue. The entire soot blowing process is automated, from environmental parameter monitoring, acoustic parameter calculation and control, to soot removal effect monitoring and acoustic parameter optimization, requiring minimal manual intervention, reducing labor costs and operational errors, and improving production efficiency. Furthermore, it can adjust the acoustic parameters in real time based on changes in the flue environment and the soot removal effect, keeping the soot blowing process in optimal condition and adapting to the cleaning needs under different working conditions.

[0007] Optionally, the step of calculating the acoustic parameters based on the environmental parameters includes: Calculate the resonant frequency of the sound wave based on the flue structure; The sound energy intensity is calculated based on the thickness and temperature of the ash accumulation in the flue. Determine the number of the acoustic wave transmitter to be activated based on the flow velocity direction angle; Based on the resonant frequency of the sound wave, the phase difference of the multiple sound sources is adjusted to achieve beam directionality.

[0008] By employing the above technical solution and calculating the acoustic resonant frequency based on the flue structure, the acoustic transmitter can emit acoustic waves that match the flue structure. When the acoustic wave frequency matches the flue's resonant frequency, resonance occurs, which more effectively removes ash from the flue wall, improving cleaning efficiency. The acoustic energy intensity is calculated based on the ash thickness and temperature within the flue, allowing adjustment of the acoustic wave energy according to the actual ash accumulation. Increasing the acoustic energy intensity when the ash is thick or the temperature is high ensures sufficient energy to loosen and remove the ash; conversely, appropriately reducing the acoustic energy intensity when the ash is thin or the temperature is low achieves the cleaning purpose while avoiding excessive energy consumption. Determining the acoustic transmitter number based on the flow velocity direction angle allows for precise selection of the appropriate transmitter location. Since flue gas flow varies at different locations within the flue, resulting in different ash distributions, this method allows for targeted cleaning of different areas, ensuring effective cleaning of all parts of the flue and reducing cleaning dead zones.

[0009] Optionally, the step of adjusting the phase difference of the multi-source sound sources according to the sound wave resonant frequency includes: Calculate vortex intensity based on velocity gradient; The corrected frequency is obtained by correcting the acoustic resonant frequency based on the vortex intensity. The phase difference of the multiple sound sources is adjusted based on the corrected frequency, the distance between the sound wave transmitter to be activated and the reference position, and the flow velocity direction angle.

[0010] By adopting the above technical solution, the velocity gradient of the airflow in the flue of a coal-fired boiler generates vortices, which affect the propagation of sound waves. Calculating the vortex intensity based on the velocity gradient and using this to correct the sound wave resonant frequency allows the sound waves to better adapt to the complex airflow environment within the flue. Adjusting the phase difference of multiple sound sources according to the corrected frequency, the distance between the sound wave transmitter to be activated and the reference position, and the velocity direction angle enables more precise superposition and interference of sound waves emitted by multiple sound wave transmitters in space. Sound wave transmitters at different locations can work collaboratively according to the actual conditions within the flue, forming directional beams that concentrate sound wave energy in the area requiring cleaning, increasing sound energy intensity, thereby more effectively removing accumulated ash, reducing cleaning dead zones, and improving overall cleaning quality.

[0011] Optionally, the intelligent acoustic soot blowing method further includes: The sound wave transmitter is controlled to work intermittently according to a set working cycle, wherein the working cycle = sound wave emission duration + cooling duration; The air source pressure of the sound wave transmitter is dynamically adjusted according to the sound energy intensity.

[0012] By adopting the above technical solutions and scheduling a cooling period after sound wave emission, the transmitter can dissipate heat, preventing overheating due to prolonged continuous operation. This reduces damage to internal components from high temperatures, slows down mechanical wear and the aging of electronic components, and thus extends the lifespan of the sound wave transmitter. Dynamically adjusting the air source pressure of the sound wave transmitter based on the sound energy intensity allows the emitted sound energy to match the ash accumulation in the flue. When the ash accumulation is thick and higher sound energy is required, increasing the air source pressure enhances the sound energy intensity and cleaning ability; conversely, when the ash accumulation is thin, decreasing the air source pressure reduces unnecessary energy output and avoids over-cleaning that could damage the flue. By rationally controlling the working cycle and adjusting the air source pressure, the sound wave transmitter maximizes energy efficiency while meeting cleaning requirements, achieving energy conservation and environmental protection.

[0013] Optionally, the step of optimizing the acoustic parameters based on the feedback regarding the dust removal effect includes: Obtain the thickness of the accumulated ash after cleaning; Calculate the rate of change of ash thickness based on the ash thickness before and after cleaning; Determine whether the rate of change of the ash accumulation thickness is less than a set maximum threshold; If not, maintain the current acoustic parameters; If so, the current acoustic parameters will be optimized.

[0014] By adopting the above technical solution, the current cleaning effect can be accurately evaluated by obtaining the ash thickness after cleaning and calculating the ash thickness change rate. When the ash thickness change rate is less than the set maximum threshold, it indicates that the current cleaning effect is poor. At this time, the acoustic parameters are optimized to make the acoustic waves act more effectively on the ash, thereby improving the cleaning efficiency and ensuring that the ash in the flue can be removed in a timely and effective manner. When the ash thickness change rate is not less than the set maximum threshold, it indicates that the cleaning effect under the current acoustic parameters is good. Maintaining the current acoustic parameters can avoid unnecessary parameter adjustments, reduce the fluctuations in the cleaning effect that may be caused by frequent parameter adjustments, ensure the stability and continuity of the cleaning process, and improve the overall cleaning efficiency.

[0015] Optionally, the steps to optimize the current acoustic parameters include: Determine whether the rate of change of the ash accumulation thickness is less than a set minimum threshold; If yes, an emergency optimization strategy is triggered, which includes generating the optimal parameter combination based on orthogonal experimental design. If no, a diagnostic adjustment strategy is triggered, which includes hardware checks and fine-tuning of parameters based on operating condition analysis.

[0016] By adopting the above technical solution, when the rate of change of ash thickness is less than the set minimum threshold, it means that the ash removal effect is extremely poor, and the ash accumulation in the flue may be very serious, affecting the normal operation of the boiler. At this point, an emergency optimization strategy is triggered, using orthogonal experimental design to generate the optimal parameter combination. Orthogonal experimental design is a highly efficient experimental design method that can find the optimal combination of multiple factors with fewer experiments. In this case, it can quickly determine the best combination of acoustic parameters such as sound frequency, sound energy intensity, and emission duration, allowing the sound waves to act more effectively on the ash accumulation, rapidly improving the ash removal effect, and avoiding boiler malfunctions or efficiency reductions caused by ash accumulation. If the rate of change of ash thickness is not less than the set minimum threshold, but is less than the set maximum threshold, further optimization is still needed, and a diagnostic adjustment strategy is triggered at this point. Hardware inspection can promptly identify any malfunctions or performance degradation in equipment such as sound wave transmitters, such as damaged transmitter speakers or unstable power supplies. Timely repair or replacement of hardware ensures normal equipment operation. Operating condition analysis can consider the impact of boiler operating load, fuel type, flue gas composition, and other factors on the ash removal effect. By combining these actual operating conditions, the sound wave parameters can be fine-tuned to make the ash removal process more precise and effective, further improving the ash removal quality.

[0017] Optionally, the steps following triggering the diagnostic adjustment strategy include: The flue was cleaned again based on the fine-tuned acoustic parameters; Calculate the rate of change of the new ash accumulation thickness; If the rate of change of the new ash thickness is less than the set maximum threshold, an emergency optimization strategy is triggered.

[0018] By adopting the above technical solution, the flue is re-cleaned based on the fine-tuned acoustic parameters, and the new ash thickness change rate is calculated, forming a dynamic ash cleaning effect feedback mechanism. When the new ash thickness change rate is less than the set maximum threshold, it indicates that the ash cleaning effect after fine-tuning is still unsatisfactory, and the ash cleaning situation may be deteriorating. At this time, an emergency optimization strategy is triggered, which can quickly take more powerful measures to improve the ash cleaning effect.

[0019] Secondly, this application provides an intelligent acoustic soot blowing system for coal-fired boiler flues, employing the following technical solution: An intelligent acoustic soot blowing system for coal-fired boiler flues includes: The environmental monitoring module is used to monitor environmental parameters inside the flue of a coal-fired boiler in real time. A sound wave transmitter, used to emit sound waves into the flue; The control module is used to calculate the sound wave parameters based on the environmental parameters, and control the sound wave transmitter to emit the required sound waves into the flue based on the sound wave parameters. The ash accumulation monitoring module is used to monitor the ash removal effect in the flue. The feedback adjustment module is used to optimize the acoustic parameters based on the feedback of the dust removal effect, so as to make the acoustic energy evenly distributed in the flue.

[0020] Thirdly, this application provides a terminal that adopts the following technical solution: A terminal, comprising: The memory stores an intelligent acoustic soot blowing program for coal-fired boiler flues. A processor is used to execute a program stored in the memory to implement the steps of the above-described intelligent acoustic soot blowing method for coal-fired boiler flues.

[0021] In summary, this application has at least the following beneficial effects: By monitoring environmental parameters within the flue in real time to calculate and control acoustic parameters, soot removal becomes more targeted, allowing for precise treatment of accumulated ash and effectively improving cleaning efficiency. Simultaneously, monitoring and optimizing acoustic parameters ensures even distribution of acoustic energy within the flue, preventing cleaning dead zones and ensuring effective removal of ash from all parts of the flue, guaranteeing overall flue cleanliness. The entire soot blowing process is automated, from environmental parameter monitoring, acoustic parameter calculation and control, to soot removal effect monitoring and acoustic parameter optimization, requiring minimal manual intervention, reducing labor costs and operational errors, and improving production efficiency. Furthermore, the ability to adjust acoustic parameters in real time based on changes in the flue environment and soot removal effect keeps the soot blowing process in optimal condition, adapting to different cleaning needs under various operating conditions. Attached Figure Description

[0022] Figure 1 This is a first flowchart of an embodiment of the method of this application; Figure 2 This is a second flowchart of an embodiment of the method of this application; Figure 3 This is a third flowchart of an embodiment of the method of this application; Figure 4 This is the fourth flowchart of an embodiment of the method of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the appendices in the embodiments of the present invention will be described below. Figure 1 - Appendix Figure 4 The technical solutions in the embodiments of the present invention are clearly and completely described herein. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] The first embodiment of this application discloses an intelligent acoustic soot blowing method for flue gas ducts of coal-fired boilers. (Refer to...) Figure 1 The intelligent acoustic soot blowing method includes S110-S150: S110, real-time monitoring of environmental parameters inside the flue of a coal-fired boiler; S120, calculates sound wave parameters based on environmental parameters; S130, based on the acoustic parameters, controls the acoustic transmitter to emit the required acoustic waves into the flue; S140, monitors the cleaning effect of ash accumulation in the flue; S150 optimizes the acoustic parameters based on feedback on the dust removal effect to ensure that acoustic energy is evenly distributed within the flue.

[0025] Reference Figure 2 S120, the steps for calculating sound wave parameters based on environmental parameters specifically include S210-S240: S210, Calculate the acoustic resonant frequency based on the flue structure; S220, calculate the sound energy intensity based on the ash thickness and temperature inside the flue; S230, determine the number of the acoustic wave transmitter to be activated based on the flow velocity direction angle; S240 adjusts the phase difference of multiple sound sources according to the sound wave resonant frequency to achieve beam directionality.

[0026] Specifically, for S240, the vortex intensity can be calculated based on the flow velocity gradient, and then the acoustic resonant frequency can be corrected based on the vortex intensity to obtain the corrected frequency. Then, according to the corrected frequency, the distance between the acoustic transmitter to be activated and the reference position and the flow velocity direction angle, the phase difference of the multiple sound sources can be adjusted to achieve beam directionality.

[0027] More specifically, environmental parameters can be collected in the S110 using multi-sensor fusion technology. For example, a set of monitoring units is arranged every 2 meters along the flue axis. Each monitoring unit includes one lidar (used to scan the flue cross-sectional profile to obtain structural parameters), two microwave radars (installed on the top and sides of the flue respectively, calculating the ash thickness through echo time difference), four thermocouples (distributed in a cross shape to measure the cross-sectional temperature field), eight hot-wire anemometers (uniformly distributed on the inner wall of the flue, inverting the flow velocity gradient through a wind speed gradient algorithm), and two two-dimensional ultrasonic anemometers (used to determine the flow velocity direction angle, with a sampling frequency of 10Hz). The raw data collected by the monitoring units is transmitted to the edge computing gateway via a 485 bus. After noise removal using a Kalman filter algorithm, a standardized environmental parameter matrix is ​​generated.

[0028] In S210, when calculating the acoustic resonant frequency based on the flue structure, the finite element modal analysis method can be used: the three-dimensional model of the flue obtained by lidar scanning is imported into COMSOL Multiphysics software, the flue wall is set as a rigid boundary condition, and the air density inside the flue is calculated according to the ideal gas law (P=ρRT), based on the Helmholtz equation. Acoustic modal analysis was performed, and the first 5 resonant frequencies were extracted as the initial candidate frequency set. The formula for calculating the fundamental frequency was simplified to f0 = c / (2L) (where L is the characteristic length of the flue). The sound velocity c was corrected for temperature and the formula was c = 331 + 0.6T. The sound velocity c was calibrated in real time.

[0029] In S220, the sound energy intensity calculation can adopt a multi-factor coupled model: First, establish the relationship model between ash accumulation thickness and sound attenuation coefficient a = 0.12δ + 0.05 (δ is the ash accumulation thickness), and then use the formula for the influence of temperature on acoustic impedance. The functional relationship between acoustic impedance and temperature is derived, where P0 is the sound pressure level of the sound source, M is the molar mass of air, γ is the specific heat capacity, T is the actual temperature, and R is a constant value; then, the empirical formula I = (P0) / (P0) is used. 2 The sound energy intensity is calculated using (2z0·a·L) / (2z0·a·L), where S is the radiation area of ​​the sound wave transmitter. Finally, the calculation results can be compensated for by a BP neural network to keep the prediction accuracy within ±3%.

[0030] In S230, the acoustic transmitter number can be determined using a direction matching algorithm: the flue section is divided into 8 sector areas (corresponding to the installation positions of 8 acoustic transmitters), each acoustic transmitter covers a 45° sector angle, and a mapping relationship is established between the flow velocity direction angle θ and the acoustic transmitter number N: N = floor((θ + 22.5°) / 45°) + 1, where θ ranges from -180° to 180°. When the flow velocity direction angle is at the boundary of two acoustic transmitter coverages (e.g., θ = 22.5°), the two adjacent acoustic transmitters are activated to form a cooperative emission mode, and power weights are allocated through a weighted algorithm.

[0031] In S240, when calculating vortex intensity, mathematical modeling is performed based on the velocity gradient tensor: Eight-point velocity data collected by a hot-wire anemometer are used to generate a continuous velocity field function v(x,y,z) through cubic spline interpolation, and the velocity gradient tensor is then calculated. Extracting the antisymmetric portion yields the vorticity vector. Its modulus is the vortex intensity Ω=||w||, when Ω>5s -1 The frequency correction mechanism is triggered at specific times.

[0032] The resonant frequency correction employs an adaptive compensation algorithm: a mapping relationship Δf = 0.02Ω·f0 is established between vortex intensity and frequency correction, where the correction coefficient 0.02 is obtained through wind tunnel calibration. The initial frequency f0 calculated in S210 is vector-synthesized with the correction Δf to obtain the corrected frequency f' = f0 + Δf, while simultaneously incorporating a temperature compensation term. (T0 is the standard temperature). Finally, the sound pressure level corresponding to the corrected frequency is verified by Fast Fourier Transform (FFT) to see if it meets the resonance condition (the sound pressure amplitude must be greater than 1.5 times the initial value).

[0033] Multi-source phase difference adjustment can be achieved using beamforming technology: A spatial coordinate model of the acoustic transmitter array is established, using a reference position 5 meters from the inlet on the central axis of the flue. The acoustic wavelength λ = c / f is calculated based on the corrected frequency. The path difference Δr between each acoustic transmitter and the reference position is calculated using the Fresnel-Kirchhoff diffraction formula. The phase difference formula is then used to... (θ0 is the flow velocity direction angle compensation term) Determine the initial phase of each acoustic transmitter. The minimum variance distortionless response (MVDR) algorithm is used to optimize the phase matrix so that the acoustic wave forms a directional beam in the target area, the main lobe width is controlled within 30°, and the side lobe suppression ratio reaches 25dB.

[0034] Finally, in S130, sound wave emission control is achieved through a distributed control system: the correction frequency f”, sound energy intensity I, and phase difference are adjusted. The control commands are packaged and transmitted via Ethernet to eight acoustic transmitters (each using a 200W piezoelectric ceramic transducer, operating in the 200Hz-2000Hz frequency range). Each acoustic transmitter is equipped with an independent DDS signal generator and power amplifier.

[0035] When controlling the sound wave transmitter to operate, the transmitter operates intermittently according to a set working cycle, where the working cycle equals the sound wave emission duration plus the cooling time. The air source pressure of the transmitter is dynamically adjusted based on the sound energy intensity. For example, if the sound energy intensity is greater than 150 dB, the air source pressure is 0.7 MPa; if the sound energy intensity is less than or equal to 120 dB, the air source pressure is 0.5 MPa.

[0036] Reference Figure 3 S150, The steps to optimize the acoustic parameters based on the feedback of the dust removal effect include S310-S350: S310, Obtain the dust accumulation thickness after dust removal; S320, calculate the rate of change of ash thickness based on the ash thickness before and after cleaning; S330, determine whether the rate of change of dust accumulation thickness is less than the set maximum threshold; S340, if not, maintain the current acoustic parameters; S350, if so, optimize the current acoustic parameters.

[0037] Reference Figure 4 The steps for optimizing the current acoustic parameters include S410-S430: S410, determine whether the rate of change of dust accumulation thickness is less than the set minimum threshold; S420, if yes, then trigger the emergency optimization strategy, which includes generating the optimal parameter combination based on the orthogonal experimental method; S430, if no, then trigger the diagnostic adjustment strategy, which includes hardware checks and fine-tuning of parameters based on operating condition analysis.

[0038] Specifically, after the acoustic transmitter stops working for a set period, such as 20 minutes, the thickness of the ash accumulation after cleaning is acquired to ensure the stability of the ash accumulation and avoid measurement errors caused by airflow disturbance during the cleaning process. The acquired data is transmitted to the controller for storage via the industrial bus. Next, the ash thickness change rate is calculated based on the ash thickness before and after cleaning. The specific formula is: Ash thickness change rate = (Thickness before cleaning - Thickness after cleaning) / Thickness before cleaning × 100%. The controller automatically completes the calculation by calling a preset algorithm module and compares the result with the maximum threshold set in the system. This maximum threshold is usually preset based on flue design parameters, fuel type, and operating experience; for example, it is generally set to 30% for coal-fired boiler flues. If the calculated ash thickness change rate is not less than the set maximum threshold (i.e., the cleaning effect is significant), the system determines that the current acoustic cleaning parameters (such as frequency, sound pressure level, and working time) are within a reasonable range and maintains the current parameters. The controller sends a parameter hold command to the acoustic transmitter to ensure operation in the original mode.

[0039] If the rate of change of dust accumulation thickness is less than the set maximum threshold (i.e., the dust removal effect is not as expected), the current acoustic parameters need to be optimized. At this time, the controller retrieves the optimal parameters under the same operating conditions from the historical operating database as the initial adjustment basis, and iteratively optimizes the system by gradually fine-tuning the acoustic frequency (±5Hz each time) and adjusting the sound energy intensity (±2dB each time), while monitoring the trend of dust accumulation thickness change in real time. Subsequently, the system further determines whether the rate of change of dust accumulation thickness is less than the set minimum threshold, which is usually set to 10% to identify extreme cases of extremely poor dust removal effect. When the rate of change of ash thickness is less than the set minimum threshold, an emergency optimization strategy is triggered. At this time, the controller automatically starts the orthogonal test module, selects three key parameters as test factors: sound wave frequency (20-80Hz), sound energy intensity (120-150dB), and working time (10-30s). Each factor is set with 3 levels, and an L9 (3^4) orthogonal test table is generated. The sound wave transmitter is controlled to run according to the 9 sets of parameters in the test table in sequence. After each set of parameters is run, the rate of change of ash thickness is collected. The influence weight of each factor on the ash cleaning effect is determined by range analysis and variance analysis. Finally, the optimal parameter combination is selected and applied immediately.

[0040] If the rate of change of ash thickness is not less than the set minimum threshold but less than the set maximum threshold, a diagnostic adjustment strategy is triggered. First, a hardware inspection process is executed. The controller sends self-check commands to key components such as the acoustic transmitter, transducer, and pipeline valves. The feedback current, voltage, and vibration signals are used to determine whether there is component damage or blockage. For example, abnormal transducer impedance may indicate that the internal ceramic plate is cracked. At the same time, real-time operating data (such as flue gas flow, temperature, and dust concentration) are analyzed. For example, if the flue gas flow is found to be 15% lower than the historical average, it is determined that the sound wave propagation may be obstructed due to local blockage of the flue. Then, the acoustic parameters are finely adjusted, such as increasing the sound energy intensity of the acoustic transmitter corresponding to the blocked area by 5-8 dB, or extending the working time to 1.5 times the original time, so as to achieve precise adjustment of the ash cleaning parameters.

[0041] The steps following triggering the diagnostic adjustment strategy include: The flue is cleaned again based on the fine-tuned acoustic parameters, and the rate of change of the new ash thickness is calculated. If the rate of change of the new ash thickness is less than the set maximum threshold, an emergency optimization strategy is triggered.

[0042] Based on the above method embodiments, the second embodiment of this application discloses an intelligent acoustic soot blowing system for coal-fired boiler flues. The intelligent acoustic soot blowing system for coal-fired boiler flues of this application embodiment can implement any of the above-described intelligent acoustic soot blowing methods for coal-fired boiler flues, and the specific working process of each module in the intelligent acoustic soot blowing system for coal-fired boiler flues can be referred to the corresponding process in the above method embodiments.

[0043] For ease of understanding, an example is as follows: An intelligent acoustic soot blowing system for coal-fired boiler flues includes: The environmental monitoring module is used to monitor environmental parameters inside the flue of a coal-fired boiler in real time. A sound wave transmitter, used to emit sound waves into the flue; The control module is used to calculate the acoustic parameters based on environmental parameters, and control the acoustic transmitter to emit the required acoustic waves into the flue based on the acoustic parameters. The ash accumulation monitoring module is used to monitor the ash removal effect in the flue. The feedback adjustment module is used to optimize the acoustic parameters based on the feedback cleaning effect, so as to make the acoustic energy evenly distributed in the flue.

[0044] A third embodiment of this application provides a terminal. As one implementation of this terminal, the terminal may include: a memory and a processor; wherein... The memory is used to store intelligent acoustic soot blowing programs for coal-fired boiler flues; The processor is used to execute the program stored in the memory to implement the steps of the intelligent acoustic soot blowing method for coal-fired boiler flues described above.

[0045] The memory can communicate with the processor via a communication bus, which can be an address bus, a data bus, a control bus, etc.

[0046] Additionally, the memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device.

[0047] Furthermore, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0048] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. An intelligent acoustic soot blowing method for a flue of a coal-fired boiler, characterized in that, The method comprises the following steps: monitoring environmental parameters in the flue of a coal-fired boiler in real time; calculating sound wave parameters according to the environmental parameters; controlling a sound wave emitter to emit required sound waves to the flue according to the sound wave parameters; monitoring the effect of soot blowing in the flue; optimizing the sound wave parameters according to the feedback of the effect of soot blowing to make the sound wave energy evenly distributed in the flue.

2. An intelligent acoustic sootblowing method for a flue of a coal-fired boiler according to claim 1, characterized in that, The step of calculating the sound wave parameters according to the environmental parameters comprises the following steps: calculating sound wave resonance frequencies according to the structure of the flue; calculating sound energy intensity according to the thickness and temperature of soot in the flue; determining the number of sound wave emitters to be started according to the flow direction angle; adjusting the phase difference of multi-source sound sources according to the sound wave resonance frequencies to realize beam orientation.

3. An intelligent acoustic sootblowing method for a flue of a coal-fired boiler according to claim 2, characterized in that, The step of adjusting the phase difference of multi-source sound sources according to the sound wave resonance frequencies comprises the following steps: calculating vortex intensity based on the flow gradient; correcting the sound wave resonance frequencies based on the vortex intensity to obtain corrected frequencies; adjusting the phase difference of multi-source sound sources according to the corrected frequencies, the distance between the sound wave emitters to be started and the reference position, and the flow direction angle.

4. A method for intelligent acoustic soot blowing of a flue of a coal-fired boiler as claimed in claim 2, wherein, The method further comprises the following steps: controlling the sound wave emitter to work intermittently according to a set working cycle, wherein the working cycle = sound wave emission time + cooling time; dynamically adjusting the air source pressure of the sound wave emitter according to the sound energy intensity.

5. A method for intelligent acoustic soot blowing of a flue of a coal-fired boiler as claimed in claim 1, wherein, The step of optimizing the sound wave parameters according to the feedback of the effect of soot blowing comprises the following steps: obtaining the thickness of soot after soot blowing; calculating the soot thickness change rate according to the thickness of soot before and after soot blowing; judging whether the soot thickness change rate is less than a set maximum threshold value; if not, maintaining the current sound wave parameters; if yes, optimizing the current sound wave parameters.

6. A method of intelligent acoustic soot blowing for a flue of a coal-fired boiler as claimed in claim 5, wherein, The step of optimizing the current sound wave parameters comprises the following steps: judging whether the soot thickness change rate is less than a set minimum threshold value; if yes, triggering an emergency optimization strategy, wherein the emergency optimization strategy comprises generating an optimal parameter combination according to an orthogonal test method; if not, triggering a diagnostic adjustment strategy, wherein the diagnostic adjustment strategy comprises hardware inspection and parameter fine-tuning according to working condition analysis.

7. A method for intelligent acoustic soot blowing of a flue of a coal-fired boiler as claimed in claim 6, wherein, The steps after triggering the diagnostic adjustment strategy comprise the following steps: reblowing the flue according to the fine-tuned sound wave parameters; calculating a new soot thickness change rate; if the new soot thickness change rate is less than the set maximum threshold value, triggering the emergency optimization strategy.

8. An intelligent acoustic sootblowing system for a coal-fired boiler flue, characterized by, The method for intelligent sound wave soot blowing in the flue of a coal-fired boiler comprises the following steps: an environmental monitoring module for monitoring environmental parameters in the flue of a coal-fired boiler in real time; a sound wave emitter for emitting sound waves to the flue; a control module for calculating sound wave parameters according to the environmental parameters and controlling the sound wave emitter to emit required sound waves to the flue according to the sound wave parameters; a soot monitoring module for monitoring the effect of soot blowing in the flue; a feedback adjustment module for optimizing the sound wave parameters according to the feedback of the effect of soot blowing to make the sound wave energy evenly distributed in the flue.

9. A terminal, characterized by comprising: The method comprises the following steps: a memory storing an intelligent sound wave soot blowing program for the flue of a coal-fired boiler; A processor for executing the program stored on the memory to implement the steps of the method for intelligent acoustic soot blowing of a flue of a coal-fired boiler as claimed in any one of claims 1-7.

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