A method for simulating, judging and cleaning up a boiler furnace coking failure
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUANENG YINGKOU THERMAL POWER CO LTD
- Filing Date
- 2026-02-06
- Publication Date
- 2026-06-19
Smart Images
Figure CN122242103A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of boiler operation optimization, specifically a method for simulating, judging, and controlling boiler furnace coking faults. Background Technology
[0002] Coking in the furnace of coal-fired boilers severely impacts the safety and economy of the unit. However, existing coking monitoring methods mainly rely on furnace outlet flue gas temperature, single-point heat flow meters, or manual observation, which have significant limitations and lags. Traditional technologies cannot accurately reconstruct the three-dimensional temperature field and ash particle movement trajectory inside the furnace, often ignoring the dynamic impact of coal quality changes on the coking mechanism, resulting in the inability to accurately locate specific coking areas. In addition, existing cleaning methods usually adopt a timed and quantitative "blind blowing" strategy, lacking the ability to target severely coking areas with variable pressure blowing, and rarely have a linkage mechanism to inhibit coking from the source by adjusting the combustion dynamic field (such as the imaginary tangential diameter), which easily leads to steam waste, tube wall damage, or coking recurrence. Summary of the Invention
[0003] To address the aforementioned problems in the existing technology, the present invention aims to provide a method for simulating, judging, cleaning, and controlling coking faults in boiler furnaces, the method comprising the following steps: Step S1: Obtain real-time operating condition data of the boiler from the distributed control system, and combine it with the coal quality industrial analysis data of the coal fed into the furnace to construct real-time boundary conditions for the gas-solid two-phase flow in the furnace.
[0004] Step S2: Load the real-time boundary conditions, use the finite volume method to discretize and solve the gas-solid two-phase flow control equations, perform a three-dimensional combustion numerical simulation inside the furnace, and calculate the motion trajectory of ash particles and the wall deposition rate distribution.
[0005] Step S3: Based on the wall deposition rate distribution, extract the local heat flux density parameters of the heated surface, and calculate the coking risk weighted index under the current operating conditions in combination with the ash melting point characteristics.
[0006] Step S4: Compare the coking risk weighted index with the preset fault classification threshold to determine the location and severity of coking on the furnace heating surface.
[0007] Step S5: Based on the severity of coking, generate graded control instructions, which include starting the soot blower for cleaning or adjusting the burner operating parameters for optimization.
[0008] Furthermore, step S2 includes the following sub-steps: Step S201: Construct a set of gas-phase control equations that include mass conservation, momentum conservation, energy conservation and component transport equations, and introduce a renormalization group turbulence model to close the Reynolds stress term.
[0009] Step S202: Solve the gas phase control equations using a semi-implicit algorithm for the pressure coupling equations to obtain the converged furnace velocity field and temperature field.
[0010] Step S203: Establish the dynamic differential equation of ash particles in the Lagrange coordinate system, solve the dynamic differential equation by fourth-order Runge-Kutta method integration, track the motion trajectory of ash particles in the velocity field of the furnace, and count the mass flux of ash particles impacting the wall.
[0011] Furthermore, in step S3, the coking risk weighted index is used to quantify the heat transfer deterioration trend and ash adhesion tendency of the heated surface, and its calculation formula is as follows: ,in, This represents the coking risk weighted index; This represents the current real-time heat flux density of the heated surface obtained through numerical simulation. This represents the design heat flux density of the heated surface under clean conditions; This indicates the near-wall flue gas temperature near the heated surface; It indicates the softening temperature of the coal ash entering the furnace and is used to characterize the melting characteristics of the ash slag; This represents the overall adhesion probability of ash particles when they impact the wall surface, and its value ranges from zero to one. , , These are the weights for heat transfer loss, temperature correlation, and adhesion probability, respectively.
[0012] Furthermore, in step S2, when calculating the motion trajectory of the ash particles, a Stokes number verification step is performed: the ratio of the relaxation time of the ash particles to the characteristic time of the local flow field is calculated to obtain the Stokes number; if the Stokes number is less than a preset following threshold, it is determined that the ash particles completely follow the airflow; if the Stokes number is greater than the preset following threshold, an inertial force term and a thermophoretic force term are introduced into the dynamic differential equation to correct the impact trajectory of the ash particles that deviates from the airflow streamline.
[0013] Furthermore, in step S2, when calculating the wall deposition rate distribution, a critical velocity capture model is used: the normal impact velocity and incident angle of the ash particles when they reach the wall are calculated; the elastic deformation energy stored in the ash particles at the moment of impact is calculated based on the Young's modulus and Poisson's ratio of the ash particles; the sum of the elastic deformation energy stored in the ash particles and the work done by the surface van der Waals forces and liquid bridge forces is compared; only when the elastic deformation energy stored in the ash particles is insufficient to overcome the surface adsorption work is it determined that the ash particles have deposited, otherwise it is determined that the ash particles have rebounded.
[0014] Furthermore, step S4 executes the region mapping logic to divide the furnace heating surface into the burner region, the burnout air region, and the screen superheater region; calculates the average and peak values of the coking risk weighted index of the grid nodes in each region; constructs a coking state matrix, maps the average value to the overall pollution level of the region, maps the peak value to local severe coking hotspots, and outputs a three-dimensional visualized coking distribution cloud map.
[0015] Furthermore, the cleaning operation of starting the sootblower in step S5 includes: Based on the location of the coking area, a target sootblower group covering the area is selected from the sootblower topology network; the superheated steam pressure parameters of the target sootblower group are read; the required jet impact force is calculated based on the severity of coking; the opening of the pressure reducing valve is adjusted to match the jet impact force, and the target sootblower group is activated sequentially in the order of following the flue gas flow direction.
[0016] Furthermore, the optimization operation of adjusting the burner operating parameters in step S5 includes: Calculate the imaginary tangent circle diameter of the furnace cross-section; if the coking area is located on the side wall of the water-cooled wall, increase the momentum ratio of the peripheral air to the primary air to shrink the imaginary tangent circle diameter, so that the high-temperature flame center is far away from the water-cooled wall; if the coking area is located at the furnace outlet, adjust the burner swing angle downward to reduce the flame center height and prolong the residence time of the flue gas in the furnace for heat exchange.
[0017] Furthermore, step S1 also includes a virtual soft measurement step: The acoustic wave transit time data of the furnace cross section is collected using an acoustic temperature sensor array; the two-dimensional temperature field distribution inside the furnace is reconstructed using a tomographic imaging algorithm; and the two-dimensional temperature field distribution is fused and corrected with the single-point thermocouple data of the distributed control system using a Kalman filter algorithm, which serves as the initial value of the temperature boundary for the numerical simulation.
[0018] Furthermore, step S5 is followed by a closed-loop verification step for the control effect: Within a preset time window after the execution of the control command, monitor the rate of change of flue gas temperature at the furnace outlet and the rate of change of main steam temperature; calculate the cleaning factor index, which is the ratio of heat absorbed by the heating surface to theoretical heat absorbed; if the increase of the cleaning factor index does not reach the preset acceptance standard, automatically increase the sensitivity of the fault classification threshold and trigger the next round of powerful soot blowing process.
[0019] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention constructs an online diagnostic model that deeply integrates physical mechanisms and real-time data. By introducing acoustic temperature measurement data to correct boundary conditions, and utilizing computational fluid dynamics algorithms that include renormalization group turbulence models and critical velocity capture models, it achieves high-fidelity simulation of the entire process of ash particles from motion trajectory and Stokes tracking verification to wall impact deposition. Combined with a coking risk weighted index that integrates heat flux density deviation, ash melting point softening temperature, and adhesion probability, the system can accurately output a three-dimensional coking distribution cloud map of the furnace heating surface, solving the problem of low monitoring accuracy and inability to locate specific coking areas in traditional methods.
[0020] This invention establishes a hierarchical control system that combines cleaning and prevention. It can not only intelligently plan target sootblower groups and dynamically adjust jet impact force according to the severity of coking to achieve efficient and targeted cleaning, but also actively shrink the imaginary tangent circle of the flame or change the height of the flame center by adjusting the momentum ratio of the perimeter air / primary air and the burner swing angle, thereby avoiding the risk of coking caused by flame adhering to the wall from the perspective of combustion dynamics. Combined with a closed-loop verification mechanism based on cleaning factor indicators, it effectively ensures the continuous cleanliness and heat exchange efficiency of the boiler heating surface. Attached Figure Description
[0021] Figure 1 This is an exemplary flowchart of the control method of the present invention.
[0022] Figure 2 This is an exemplary flowchart of the calculation and solution steps of the present invention. Detailed Implementation
[0023] The present invention will be further described below with reference to specific embodiments.
[0024] like Figure 1 As shown, this invention provides a method for simulating, judging, cleaning, and controlling boiler furnace coking faults. The method includes the following steps: Step S1: Obtain real-time operating condition data of the boiler from the distributed control system, and construct real-time boundary conditions for the gas-solid two-phase flow in the furnace by combining the coal quality industrial analysis data of the coal fed into the furnace. The real-time operating condition data includes key operating parameters such as coal feed rate, air volume at each stage, primary air pressure, feedwater temperature, and main steam flow rate. The coal quality industrial analysis data includes at least volatile matter, fixed carbon, sulfur content, and lower heating value, which are used to calculate the chemical heat release rate of fuel combustion and flue gas composition.
[0025] Step S1 also includes a virtual soft measurement step: The acoustic wave transit time data of the furnace cross-section was acquired using an array of acoustic temperature sensors. A tomographic imaging algorithm was used to reconstruct the two-dimensional temperature field distribution inside the furnace. A Kalman filter algorithm was then used to fuse and correct the two-dimensional temperature field distribution with single-point thermocouple data from the distributed control system, serving as the initial temperature boundary value for numerical simulation. The fusion of multi-source heterogeneous data effectively eliminated the hysteresis and measurement noise of single temperature measurement methods.
[0026] Step S2: Load real-time boundary conditions, use the finite volume method to discretize and solve the gas-solid two-phase flow control equations, perform a three-dimensional combustion numerical simulation inside the furnace, and calculate the motion trajectory of ash particles and the wall deposition rate distribution. The numerical simulation uses a structured mesh to discretize the furnace geometric model, and uses a pressure-based solver to iteratively calculate the velocity field and pressure field.
[0027] like Figure 2 As shown, step S2 in this embodiment includes the following sub-steps: Step S201: Construct a set of gas-phase control equations that include mass conservation, momentum conservation, energy conservation and component transport equations, and introduce a renormalization group turbulence model to close the Reynolds stress term.
[0028] Step S202: The semi-implicit algorithm of the pressure coupling equations is used to solve the gas phase control equations to obtain the converged furnace velocity field and temperature field.
[0029] Step S203: Establish the dynamic differential equation of ash particles in the Lagrange coordinate system, solve the dynamic differential equation by fourth-order Runge-Kutta method integration, track the motion trajectory of ash particles in the furnace velocity field, and count the mass flux of ash particles impacting the wall.
[0030] In step S2, when calculating the trajectory of the ash particles, a Stokes number verification step is performed: the ratio of the relaxation time of the ash particles to the characteristic time of the local flow field is calculated to obtain the Stokes number; if the Stokes number is less than the preset following threshold, it is determined that the ash particles completely follow the airflow; if the Stokes number is greater than the preset following threshold, an inertial force term and a thermophoretic force term are introduced into the dynamic differential equation to correct the impact trajectory of the ash particles that deviates from the airflow streamline.
[0031] Step S2 employs a critical velocity capture model when calculating the wall deposition rate distribution: It calculates the normal impact velocity and incident angle of ash particles upon reaching the wall; based on the Young's modulus and Poisson's ratio of the ash particles, it calculates the elastic deformation energy stored at the moment of impact; it compares the sum of the elastic deformation energy stored with the work done by surface van der Waals forces and liquid bridging forces; only when the elastic deformation energy stored is insufficient to overcome the surface adsorption work is it determined that ash particles have deposited; otherwise, it is determined that ash particles have rebounded. This physical judgment logic fully considers the microscopic mechanism of surface adhesion, making the deposition calculation more consistent with actual boiler operation.
[0032] Step S3: Based on the wall deposition rate distribution, extract the local heat flux density parameters of the heated surface, and calculate the coking risk weighted index under the current operating conditions in combination with the ash melting point characteristics.
[0033] In step S3, the coking risk weighted index is used to quantify the heat transfer deterioration trend and ash adhesion tendency of the heating surface. Its calculation formula is as follows: ,in, This represents a weighted index for coking risk; This represents the current real-time heat flux density of the heated surface obtained through numerical simulation. This represents the design heat flux density of the heated surface under clean conditions; This indicates the near-wall flue gas temperature near the heated surface; It indicates the softening temperature of the coal ash entering the furnace and is used to characterize the melting characteristics of the ash slag; This represents the overall adhesion probability of ash particles when they impact the wall surface, and its value ranges from zero to one. , , These are the weights for heat transfer loss, temperature correlation, and adhesion probability, respectively.
[0034] In the formula of this embodiment, the first term By monitoring the decrease in heat flux density, the thermal resistance effect caused by the increase in coke thickness was characterized. Its beneficial effect lies in its ability to capture the minute heat transfer responses in the early "ash accumulation" stage, providing an early warning before visible "coking" occurs. (Second item) A correlation was established between ambient flue gas temperature and coal ash melting characteristics. Its beneficial effect lies in quantifying the physical evolution process of ash transitioning from "dry ash" to a "molten state." As this ratio increases, the index can rapidly detect the nonlinear increase in ash viscosity, thereby guiding the system to perform a "soft cleaning" operation before the coke hardens, avoiding the formation of hard coke that is difficult to blow off and reducing maintenance costs. (Third item) Combining the aforementioned particle phase dynamics simulation results, the impact capture probability at the microscopic level was characterized. Its beneficial effect lies in eliminating instantaneous false dust accumulation caused by flow field disturbances, removing false alarms through a mechanistic model, and ensuring the accuracy of control commands. This is achieved through weighting factors. With its flexible configuration, this formula can adapt to the coking characteristics of different coal types, solving the problem that traditional monitoring methods have fixed thresholds and cannot cope with complex coal type changes.
[0035] Step S4 compares the coking risk weighted index with a preset fault classification threshold to determine the location and severity of coking on the furnace heating surface. Step S4 also executes region mapping logic to divide the furnace heating surface into burner, burnout air, and screen-type superheater regions. The average and peak values of the coking risk weighted index for each grid node within each region are calculated. A coking state matrix is constructed, mapping the average value to the overall regional contamination level and the peak value to localized severe coking hotspots, and outputting a three-dimensional visualized coking distribution cloud map. This mapping logic realizes the logical transformation from grid data to macroscopic operating status.
[0036] Step S5: Based on the severity of coking, generate graded control instructions, which include starting the soot blower for cleaning or adjusting the burner operating parameters for optimization.
[0037] Step S5, which involves starting the sootblower for cleaning, includes: Based on the location of the coking area, the target sootblower group covering the area is selected from the sootblower topology network; the superheated steam pressure parameters of the target sootblower group are read; the required jet impact force is calculated based on the severity of coking; the opening of the pressure reducing valve is adjusted to match the jet impact force, and the target sootblower group is activated sequentially in the order of flue gas flow direction.
[0038] Step S5 involves performing optimization operations to adjust the burner operating parameters, including: Calculate the imaginary tangent circle diameter of the furnace cross-section; if the coking area is located on the side wall of the water-cooled wall, increase the momentum ratio of the peripheral air to the primary air to shrink the imaginary tangent circle diameter and move the high-temperature flame center away from the water-cooled wall; if the coking area is located at the furnace outlet, adjust the burner swing angle downward to reduce the flame center height and prolong the residence time of the flue gas in the furnace for heat exchange.
[0039] Step S5 is followed by a closed-loop verification step for the control effect: Within a preset time window after the control command is executed, the rate of change of flue gas temperature at the furnace outlet and the rate of change of main steam temperature are monitored; the cleaning factor index is calculated, which is the ratio of heat absorbed by the heating surface to the theoretical heat absorbed; if the increase in the cleaning factor index does not reach the preset acceptance standard, the sensitivity of the fault classification threshold is automatically increased, and the next round of powerful soot blowing process is triggered.
[0040] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
Claims
1. A method for simulating, judging, cleaning, and controlling coking faults in a boiler furnace, characterized in that, The method includes the following steps: Step S1: Obtain real-time operating condition data of the boiler from the distributed control system, and combine it with the coal quality industrial analysis data of the coal fed into the furnace to construct real-time boundary conditions for the gas-solid two-phase flow in the furnace. Step S2: Load the real-time boundary conditions, use the finite volume method to discretize and solve the gas-solid two-phase flow control equations, perform a three-dimensional combustion numerical simulation inside the furnace, and calculate the motion trajectory of ash particles and the wall deposition rate distribution. Step S3: Based on the wall deposition rate distribution, extract the local heat flux density parameters of the heated surface, and calculate the coking risk weighted index under the current working condition in combination with the ash melting point characteristics. Step S4: Compare the coking risk weighted index with the preset fault classification threshold to determine the location and severity of coking on the furnace heating surface. Step S5: Based on the severity of coking, generate graded control instructions, which include starting the soot blower for cleaning or adjusting the burner operating parameters for optimization.
2. The method for simulating, judging, and controlling coking faults in a boiler furnace according to claim 1, characterized in that, Step S2 includes the following sub-steps: Step S201: Construct a set of gas-phase control equations that include mass conservation, momentum conservation, energy conservation and component transport equations, and introduce a renormalization group turbulence model to close the Reynolds stress term; Step S202: The gas phase control equations are solved using a semi-implicit algorithm of the pressure coupling equations to obtain the converged furnace velocity field and temperature field. Step S203: Establish the dynamic differential equation of ash particles in the Lagrange coordinate system, solve the dynamic differential equation by fourth-order Runge-Kutta method integration, track the motion trajectory of ash particles in the velocity field of the furnace, and count the mass flux of ash particles impacting the wall.
3. The method for simulating, judging, cleaning, and controlling coking faults in a boiler furnace according to claim 1, characterized in that, In step S3, the coking risk weighted index is used to quantify the heat transfer deterioration trend and ash adhesion tendency of the heated surface, and its calculation formula is as follows: ,in, This represents the coking risk weighted index; This represents the current real-time heat flux density of the heated surface obtained through numerical simulation. This represents the design heat flux density of the heated surface under clean conditions; This indicates the near-wall flue gas temperature near the heated surface; It indicates the softening temperature of the coal ash entering the furnace and is used to characterize the melting characteristics of the ash slag; This represents the overall adhesion probability of ash particles when they impact the wall surface, and its value ranges from zero to one. , , These are the weights for heat transfer loss, temperature correlation, and adhesion probability, respectively.
4. The method for simulating, judging, and controlling coking faults in a boiler furnace according to claim 1, characterized in that, In step S2, when calculating the trajectory of the ash particles, a Stokes number verification step is performed: the ratio of the relaxation time of the ash particles to the characteristic time of the local flow field is calculated to obtain the Stokes number; if the Stokes number is less than a preset following threshold, it is determined that the ash particles completely follow the airflow; if the Stokes number is greater than the preset following threshold, an inertial force term and a thermophoretic force term are introduced into the dynamic differential equation to correct the impact trajectory of the ash particles that deviates from the airflow streamline.
5. The method for simulating, judging, and controlling coking faults in a boiler furnace according to claim 1, characterized in that, In step S2, when calculating the deposition rate distribution on the wall, a critical velocity capture model is used: the normal impact velocity and incident angle of the ash particles when they reach the wall are calculated; the elastic deformation energy stored in the ash particles at the moment of impact is calculated based on the Young's modulus and Poisson's ratio of the ash particles; the sum of the elastic deformation energy stored in the ash particles and the work done by the surface van der Waals forces and liquid bridge forces is compared; only when the elastic deformation energy stored in the ash particles is insufficient to overcome the surface adsorption work is it determined that the ash particles have deposited, otherwise it is determined that the ash particles have rebounded.
6. The method for simulating, judging, cleaning, and controlling coking faults in a boiler furnace according to claim 1, characterized in that, Step S4 executes the region mapping logic, dividing the furnace heating surface into burner region, burnout air region and screen superheater region; calculates the average and peak values of the coking risk weighted index of the grid nodes in each region; constructs a coking state matrix, maps the average value to the overall pollution level of the region, maps the peak value to local severe coking hotspots, and outputs a three-dimensional visualized coking distribution cloud map.
7. The method for simulating, judging, and controlling coking faults in a boiler furnace according to claim 1, characterized in that, The cleaning operation of starting the soot blower in step S5 includes: Based on the location of the coking area, a target sootblower group covering the area is selected from the sootblower topology network; the superheated steam pressure parameters of the target sootblower group are read; the required jet impact force is calculated based on the severity of coking; the opening of the pressure reducing valve is adjusted to match the jet impact force, and the target sootblower group is activated sequentially in the order of following the flue gas flow direction.
8. The method for simulating, judging, cleaning, and controlling coking faults in a boiler furnace according to claim 1, characterized in that, The optimization operation of adjusting the burner operating parameters in step S5 includes: Calculate the imaginary tangent circle diameter of the furnace cross-section; if the coking area is located on the side wall of the water-cooled wall, increase the momentum ratio of the peripheral air to the primary air to shrink the imaginary tangent circle diameter, so that the high-temperature flame center is far away from the water-cooled wall; if the coking area is located at the furnace outlet, adjust the burner swing angle downward to reduce the flame center height and prolong the residence time of the flue gas in the furnace for heat exchange.
9. The method for simulating, judging, cleaning, and controlling coking faults in a boiler furnace according to claim 1, characterized in that, Step S1 further includes a virtual soft measurement step: The acoustic wave transit time data of the furnace cross section is collected using an acoustic temperature sensor array; the two-dimensional temperature field distribution inside the furnace is reconstructed using a tomographic imaging algorithm; and the two-dimensional temperature field distribution is fused and corrected with the single-point thermocouple data of the distributed control system using a Kalman filter algorithm, which serves as the initial value of the temperature boundary for the numerical simulation.
10. The method for simulating, judging, cleaning, and controlling coking faults in a boiler furnace according to claim 1, characterized in that, Following step S5, a closed-loop verification step for the control effect is also included: Within a preset time window after the control command is executed, monitor the rate of change of flue gas temperature at the furnace outlet and the rate of change of main steam temperature. Calculate the cleaning factor index, which is the ratio of the heat absorbed by the heated surface to the theoretical heat absorbed. If the increase in the cleaning factor index does not reach the preset acceptance standard, the sensitivity of the fault classification threshold will be automatically increased, and the next round of powerful dust blowing process will be triggered.