A boiler anti-wear and explosion-proof detection and early warning method and system

Abstract

The present invention discloses a method and system for boiler anti-wear and explosion-proof detection and early warning, which relates to the technical field of boiler detection, and includes: arranging sensors to collect boiler operation data; calculating the temperature field inside the boiler through a heat flow coupling equation; calculating the flow velocity field inside the boiler by using a fluid mechanics equation; based on the temperature field and the flow velocity field, combining the stress field distribution of boiler components in the working environment, constructing a damage degree equation to evaluate the damage condition of boiler components; based on the calculation results of the damage degree equation, judging the damage risk level of boiler components, performing risk early warning operations, and adjusting the operation parameters of the boiler. The boiler anti-wear and explosion-proof detection and early warning method provided by the present invention comprehensively considers the multi-physical field effects inside the boiler, and can more accurately evaluate the damage state and potential risks of boiler components. By using the finite element method to finely solve the temperature field and the flow velocity field, combining the stress field distribution, dynamically evaluating the damage degree of the boiler, and reflecting the health state of boiler components in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of boiler detection, and specifically provides a method and system for detecting, preventing wear and explosion, and giving early warning of boilers. Background Art

[0002] Boilers play a crucial role in various industrial productions, especially in the fields of power generation, chemical industry, metallurgy, etc. The long-term operation of boilers faces various harsh environments, such as high temperature, high pressure, and corrosive fluids. These factors can cause damage to boiler components, such as furnace tubes, combustion chambers, heat exchangers, etc., and even affect the safety and stability of boilers. Currently, boiler monitoring technologies mainly rely on the monitoring of basic parameters of devices such as temperature sensors and pressure sensors, as well as methods such as regular manual inspections and non-destructive testing technologies.

[0003] However, traditional temperature and pressure monitoring can only provide some operating parameters of boilers and cannot comprehensively reflect the damage conditions of internal components. Especially under extreme conditions such as high temperature and high pressure, traditional methods are prone to missing detections or failing to detect minor damages.

[0004] Existing manual inspection methods are limited by the experience of inspectors and inspection cycles, making it difficult to achieve real-time and dynamic monitoring, and the comprehensiveness and accuracy of detections are poor.

[0005] Although non-destructive testing technologies have been applied in the damage assessment of boilers, these methods still require manual intervention and cannot provide comprehensive real-time data.

[0006] In summary, existing boiler monitoring technologies cannot comprehensively evaluate various operating states of boilers and are difficult to accurately and in real time judge the damage conditions of boiler components. Summary of the Invention

[0007] In view of the above existing problems, the present invention is proposed.

[0008] Therefore, the technical problem solved by the present invention is that existing technologies cannot comprehensively evaluate the damage conditions of boiler components, only rely on the calculation of a single physical quantity, lack coupled analysis, and do not comprehensively consider the damage mechanism of boiler components.

[0009] To solve the above technical problem, the present invention provides the following technical solution: A method for detecting, preventing wear and explosion, and giving early warning of boilers, including:

[0010] Arranging sensors to collect boiler operation data;

[0011] Calculating the temperature field inside the boiler through a heat flow coupling equation;

[0012] Calculating the flow velocity field inside the boiler by using a fluid mechanics equation;

[0013] Based on the stress field distribution of boiler components in the working environment combined with the temperature field and flow velocity field, a damage degree equation is constructed to evaluate the damage condition of boiler components;

[0014] Based on the calculation results of the damage degree equation, judge the damage risk level of boiler components, perform risk early warning operations, and adjust the operating parameters of the boiler;

[0015] Calculating the temperature field inside the boiler includes discretizing the spatial domain of the boiler, dividing it into multiple finite elements based on the geometric shape of the boiler components, and each element is connected by nodes to form a discretized grid;

[0016] Based on the physical characteristics of the boiler, the finite element method is used as the numerical solution method of the heat flow coupling equation to convert the continuous temperature field into temperature values at discrete points and calculate at each node;

[0017] Set the temperature field boundary conditions, which include heat source boundary conditions, convective heat transfer boundary conditions, and initial temperature conditions;

[0018] According to the discretized grid structure, construct the heat flow coupling equation for each unit, perform iterative calculations through the temperature distribution of the nodes, and solve the temperature value of each node at each time step;

[0019] Through the time domain analysis of the finite element method, perform iterative calculations on the temperature field. Within each time step, update the temperature value of each node according to the temperature distribution information of the current time step to obtain the dynamic temperature field of the boiler during the entire working cycle.

[0020] As a preferred solution of the boiler anti-wear and explosion-proof detection and early warning method described in the present invention, wherein: the heat flow coupling equation is expressed as

[0021]

[0022] Among them, T(x,t) represents the temperature at a certain position x and time t inside the boiler; α represents the thermal conductivity coefficient, which describes the thermal conduction characteristics of the material; represents the second-order gradient of the temperature field, which describes the change of temperature in space; β represents the coefficient of coupling between fluid flow and temperature field, which characterizes the influence of fluid flow on heat conduction; v(x,t) represents the velocity field of the fluid.

[0023] As a preferred solution of the boiler anti-wear and explosion-proof detection and early warning method described in the present invention, wherein: the fluid mechanics equation is expressed as

[0024]

[0025] Among them, v(x,t) represents the fluid velocity field, describing the distribution of the fluid velocity in space x and time t; ρ represents the density of the fluid; p represents the pressure of the fluid; μ represents the viscosity of the fluid; γ represents the influence coefficient of temperature on the fluid flow rate.

[0026] As a preferred embodiment of the boiler anti-abrasion and explosion-proof detection and early warning method described in the present invention, wherein: calculating the flow velocity field inside the boiler includes discretizing the spatial domain of the boiler, dividing it into multiple finite elements based on the geometric shape of the boiler components, and each element is connected by nodes to form a discretized grid;

[0027] Based on the physical characteristics of the boiler, using the finite element method as the numerical solution of the fluid mechanics equation, converting the fluid velocity field into the flow velocity values of discrete points, and calculating at each node;

[0028] Setting the boundary conditions of the flow velocity field, the boundary conditions of the flow velocity field include the velocity boundary condition at the fluid inlet, the temperature boundary condition at the fluid inlet, and the fluid pressure boundary condition at the outlet;

[0029] According to the discretized grid structure, constructing the fluid mechanics equation of each unit to obtain the flow velocity field distribution of each node;

[0030] Through the time-domain analysis of the finite element method, performing iterative calculations on the flow velocity field. In each time step, updating the fluid velocity field according to the current temperature field and fluid characteristics until the convergence condition is met, obtaining the dynamic flow velocity field of the boiler during the entire working cycle.

[0031] As a preferred embodiment of the boiler anti-abrasion and explosion-proof detection and early warning method described in the present invention, wherein: the damage degree equation is expressed as,

[0032]

[0033] Among them, D(t) represents the damage condition of the boiler component at time t; σ(t) represents the stress borne at time t; σ max represents the maximum bearing stress of the material; T max represents the highest working temperature of the material; v max represents the maximum flow velocity; n, m, p respectively represent the sensitivity indices of stress, temperature, and flow velocity to material damage.

[0034] As a preferred embodiment of the boiler anti-abrasion and explosion-proof detection and early warning method described in the present invention, wherein: judging the damage risk level of the boiler component includes when D(t) is less than or equal to the first damage threshold, judging it as a low risk level, when D(t) is greater than the first damage threshold and less than the second damage threshold, judging it as a medium risk level, and when D(t) is greater than or equal to the second damage threshold, judging it as a high risk level.

[0035] As a preferred solution of the boiler anti-abrasion and explosion-proof detection and early warning method described in the present invention, where: when it is judged to be a low-risk level, the operating parameters of the current boiler are maintained;

[0036] When it is judged to be a medium-risk level, the discretized grid is optimized to increase the grid density;

[0037] The encrypted discretized grid is used to update the temperature field, flow velocity field and damage degree equation;

[0038] The damage degree distribution under grid encryption is obtained, abnormal grids are determined, and the operating parameters of boiler components within the abnormal grids are adjusted;

[0039] When it is judged to be a high-risk level, according to the damage degree distribution, the area that needs to be shut down is determined, and local shutdown operations are performed through a zoning control strategy;

[0040] Within the shutdown area, acoustic emission detection technology is used to monitor the wear of boiler components and identify the worn parts; at the same time, infrared thermal imaging technology is used to monitor temperature anomalies in the combustion area, heat exchange area and cooling area; and vibration monitoring technology is used to evaluate the dynamic state of boiler components;

[0041] According to the detection results, the parts with wear or explosion-proof hidden dangers are identified;

[0042] Spray a wear-resistant coating or perform welding repair on the worn parts, reinforce and seal the parts with explosion-proof hidden dangers, and adjust the fluid flow rate, temperature control and pressure parameters; after the repair is completed, restart the boiler.

[0043] Another object of the present invention is to provide a boiler anti-abrasion and explosion-proof detection and early warning system, which can solve the problem that the existing boiler anti-abrasion and explosion-proof monitoring methods lack effective coupling analysis between the temperature field, flow velocity field and damage degree, resulting in the inability to accurately evaluate the damage risk of boiler components. At the same time, the existing methods have low calculation efficiency when dealing with complex physical field coupling problems.

[0044] To solve the above technical problems, the present invention provides the following technical solution: a boiler anti-abrasion and explosion-proof detection and early warning system, including: a data acquisition module for arranging sensors to collect boiler operation data; a temperature analysis module for calculating the temperature field inside the boiler through a heat flow coupling equation; a flow velocity analysis module for calculating the flow velocity field inside the boiler using a fluid mechanics equation; a damage analysis module for constructing a damage degree equation based on the temperature field and flow velocity field in combination with the stress field distribution of boiler components in the working environment to evaluate the damage condition of boiler components; and a warning and adjustment module for judging the damage risk level of boiler components based on the calculation results of the damage degree equation, performing risk warning operations, and optimizing and adjusting the operating parameters of the boiler.

[0045] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the boiler anti-abrasion and explosion-proof detection and early warning method described above are implemented.

[0046] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, the steps of the boiler anti-abrasion and explosion-proof detection and early warning method described above are implemented.

[0047] Advantages of the present invention: The boiler anti-abrasion and explosion-proof detection and early warning method provided by the present invention optimizes the detection and early warning process of boiler anti-abrasion and explosion-proof by introducing a damage degree evaluation method based on the coupling of temperature field, flow velocity field and stress field. Compared with the traditional single physical field analysis, the present invention comprehensively considers the multi-physical field effects inside the boiler and can more accurately evaluate the damage state and potential risks of boiler components. By using the finite element method to finely solve the temperature field and flow velocity field and combining with the stress field distribution, the damage degree of the boiler is dynamically evaluated, which can reflect the health state of boiler components in real time.

[0048] In terms of risk assessment, the present invention proposes a multi-level risk division method based on the damage degree. At the low risk level, maintain the existing operating state of the boiler and avoid unnecessary operation interventions; at the medium risk level, improve the calculation accuracy through a local grid encryption optimization scheme, optimize the boiler operating parameters, and reduce potential damage; at the high risk level, combine the risk assessment results, perform local shutdown and implement repairs, thereby preventing serious failures of boiler equipment due to damage.

[0049] The advantage of the present invention compared with the traditional technology is that it can not only more accurately evaluate the damage of boiler components, but also take targeted optimization measures according to different risk levels, avoiding untimely early warning or improper handling caused by over-reliance on traditional monitoring means. By adopting the method of the present invention, the operating safety and efficiency of the boiler are significantly improved, while the service life of the equipment is extended and the shutdown and maintenance costs are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 It is the overall flowchart of a boiler anti-abrasion and explosion-proof detection and early warning method provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0053] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0054] Example 1, referring to Figure 1 , which is an embodiment of the present invention, provides a method for detecting and warning against abrasion and explosion of a boiler, including:

[0055] Step 1: Arrange sensors to collect boiler operation data;

[0056] Step 2: Calculate the temperature field inside the boiler through the heat-fluid coupling equation;

[0057] Step 3: Calculate the flow velocity field inside the boiler using the fluid mechanics equation;

[0058] Step 4: Based on the temperature field and the flow velocity field, combined with the stress field distribution of boiler components in the working environment, construct a damage degree equation to evaluate the damage condition of boiler components;

[0059] Step 5: Based on the calculation results of the damage degree equation, determine the damage risk level of boiler components, perform risk warning operations, and adjust the operation parameters of the boiler.

[0060] In Step 2, the heat-fluid coupling equation is expressed as,

[0061]

[0062] where T(x,t) represents the temperature at a certain position x and time t inside the boiler; α represents the thermal conductivity coefficient, describing the thermal conduction characteristics of the material; represents the second-order gradient of the temperature field, describing the change of temperature in space; β represents the coefficient of coupling between fluid flow and the temperature field, characterizing the influence of fluid flow on heat conduction; v(x,t) represents the velocity field of the fluid.

[0063] It should be noted that in this embodiment, a local heat source model is first constructed to describe the distribution of the heat source density inside the boiler, expressed as:

[0064]

[0065] Among them, q(x,t) is the heat source density at a certain position x and time t inside the boiler, representing the heat energy generation density at this position and moment. q0 is the initial heat source density of the boiler, representing the heat source distribution of the boiler under standard working conditions, obtained from boiler design parameters or experimental data. ΔT(x,t) is the temperature change at a certain position x and time t inside the boiler, representing the temperature difference between this position and the target temperature, defined as ΔT(x,t) = T(x,t) - T target (x,t), where T(x,t) is the actual temperature at this position, and T target (x,t) is the target temperature at this position, and T max is the maximum working temperature of the boiler, determined by the materials and design limitations of the boiler, usually the highest safe temperature for boiler operation.

[0066] Furthermore, calculating the temperature field inside the boiler includes discretizing the spatial domain of the boiler. Based on the geometric shape of the boiler components, it is divided into multiple finite elements, and each element is connected by nodes to form a discretized grid;

[0067] Based on the physical properties of the boiler, the finite element method is used as the numerical solution of the heat flow coupling equation to convert the continuous temperature field into temperature values at discrete points and calculate at each node;

[0068] Set the temperature field boundary conditions. The temperature field boundary conditions include heat source boundary conditions, convective heat transfer boundary conditions, and initial temperature conditions. Among them, the heat source boundary conditions reflect the heat input inside the boiler, and the cooling area is modeled through the convective heat transfer boundary conditions;

[0069] According to the discretized grid structure, construct the heat flow coupling equation for each element, and perform iterative calculations through the temperature distribution of the nodes to solve the temperature values of each node at each time step;

[0070] Through the time domain analysis of the finite element method, perform iterative calculations on the temperature field. Within each time step, update the temperature values of each node according to the temperature distribution information of the current time step to obtain the dynamic temperature field of the boiler during the entire working cycle.

[0071] It should be noted that the thermal conductivity coefficient α represents the heat conduction performance of the material under the action of heat flow, which determines the propagation speed of temperature inside the boiler. To calculate the thermal conductivity coefficient, it is derived by combining the local heat source model and the temperature difference of the temperature field, expressed as:

[0072]

[0073] Among them, α(x,t) is the thermal conductivity at a certain position x and time t inside the boiler, representing the heat conduction ability at that position, and ΔT(x,t) is the temperature difference at a certain position x and time t inside the boiler, representing the difference between the actual temperature and the target temperature at that position.

[0074] To further optimize the thermal conductivity, the least squares method is introduced, and the thermal conductivity is adjusted by minimizing the error between the internal temperature field of the boiler and the target temperature field, which is expressed as:

[0075]

[0076] Among them, Error T is the temperature field error, representing the total error between the target temperature field and the actual temperature field, T target (x,t) is the target temperature field, preset based on boiler design or experimental data, and T(x,t) is the actual temperature field.

[0077] By minimizing the error, a more accurate thermal conductivity α(x,t) is obtained, which is expressed as:

[0078] α opt (x,t) = α0 + k·(T target (x,t) - T(x,t))

[0079] Among them, α opt (x,t) is the optimized thermal conductivity, α0 is the initial thermal conductivity, and k is the learning rate.

[0080] The coupling coefficient β reflects the coupling strength between the fluid flow rate and the temperature field, which is expressed as:

[0081]

[0082] Among them, β(x,t) is the coupling coefficient at a certain position x and time t inside the boiler, and ΔT(x,t) is the temperature difference, representing the difference between the actual temperature and the target temperature at that position.

[0083] The particle swarm optimization algorithm is used to optimize the coupling coefficient β to obtain the optimal coupling effect, which is expressed as:

[0084]

[0085] Among them, Error β is the coupling coefficient error, and βtarget(x,t) is the target coupling coefficient.

[0086] Through the optimization process, the following optimal coupling coefficient is finally obtained, which is expressed as:

[0087]

[0088] Specifically, the purpose of discretizing the boiler space domain is to transform the continuous physical system into a discrete mathematical model for numerical calculation. The specific steps are as follows:

[0089] First, establish a three-dimensional geometric model of the boiler. This model can be designed using three-dimensional modeling tools. The geometric model should include a combustion zone, a heat exchange zone, and a cooling zone. Through this model, the characteristics of different regions can be identified, and thus the grid density can be reasonably arranged.

[0090] In the combustion zone, due to the very steep temperature gradient during the combustion process, a high calculation accuracy is required. To ensure the calculation accuracy, a refined grid is adopted. At this time, the side length of each grid cell will be relatively small, for example, set to 5 cm or less. In the heat exchange zone, the temperature field changes relatively uniformly in this part, and a coarser grid can be adopted to reduce the calculation amount. The grid density can be set from 10 cm to 15 cm. In the cooling zone, the temperature field in the cooling zone changes less, so the grid spacing can be further increased, and the grid density is set to about 20 cm.

[0091] The combustion zone grid uses tetrahedral elements, and its advantage is that it can adapt to complex geometric shapes. In the combustion zone, the adaptive grid refinement technology can be used to increase the detail accuracy. The heat exchange zone grid uses hexahedral elements, and this kind of grid can provide higher calculation efficiency under regular geometric structures. The cooling zone grid can use tetrahedral or hexahedral elements, depending on the specific geometric shape.

[0092] For the regions with large temperature field changes in the combustion zone, use the adaptive grid refinement method. This method adjusts the grid density according to the preliminary calculation results to ensure the accuracy of important regions.

[0093] Calculate the temperature gradient: Calculate the rate of change of the temperature field in each grid cell. If the gradient value exceeds the threshold, refine the grid.

[0094]

[0095] Among them, Grad(T) represents the gradient of the temperature field T at a certain position in space, which describes the rate of change of the temperature field along the spatial direction. T i+1 represents the temperature value at the next node of the current grid cell, usually the temperature at position i + 1. T i represents the temperature value at the current node of the current grid cell, usually the temperature at position i. Δx represents the spatial distance between the current grid cells.

[0096] In the regions with large gradients, grid refinement is carried out. For example, in the combustion zone, if the temperature gradient is greater than the set threshold, the number of grids in this region is increased to ensure the accuracy of the numerical solution.

[0097] To avoid calculation errors caused by mesh quality problems (such as distortion or degeneration), when generating the mesh, it is necessary to follow the mesh quality standard: the minimum angle of the mesh should be greater than 30 degrees. The shape factor of the mesh is close to 1 to avoid distorted meshes.

[0098] Furthermore, the boiler space domain Ω needs to be discretized into multiple small elements. According to the actual geometry of the boiler, a suitable mesh type is selected, such as tetrahedrons, hexahedrons, etc. The mesh density should be selected according to the calculation accuracy requirements: for high-temperature gradient regions, the mesh density should be increased; for regions with relatively gentle temperature changes, the mesh density can be appropriately reduced. Let Ω e be a single small element, and the temperature field T(x,t) is interpolated within each element through the shape functions of the nodes.

[0099] The shape function selected is the first-order linear shape function N i (x) to interpolate the temperature field T(x,t), expressed as:

[0100]

[0101] where T(x,t) represents the temperature at the boiler space position x and time t, N i (x) represents the shape function of the i-th node, satisfying N i (x j ) = δ ij , that is, at node j, the shape function N i (x) is 1, and 0 at other nodes, and T i (t) represents the temperature of the i-th node at time t.

[0102] The Laplace operator in the heat-fluid coupling equation needs to be discretized, and the finite element method is used to calculate the stiffness matrix of each element. The discretization of the Laplace operator is:

[0103]

[0104] where K ij is the element stiffness matrix, representing the heat conduction relationship between node i and node j, represents the second-order gradient of the shape function.

[0105] The specific calculation method of the stiffness matrix is:

[0106]

[0107] The fluid coupling term in the heat-fluid coupling equation describes the influence of fluid flow on the temperature field. This term is discretized to obtain the fluid coupling matrix:

[0108]

[0109] Among them, M ij represents the fluid coupling matrix, which describes the coupling effect between fluid flow and temperature field. The calculation of the fluid coupling matrix M ij needs to consider the fluid velocity field v(x,t) and is discretized by numerical methods such as the weighted least squares method.

[0110] To improve stability and accurately describe the evolution of the temperature field over time, the implicit time discretization method is selected. In this embodiment, the backward Euler method is used for time discretization. In the backward Euler method, the time derivative term is discretized at the current time step t n+1 to obtain:

[0111]

[0112] Among them, T n+1 represents the temperature field at the current time step t n+1 moment, T n represents the temperature field at the previous time step t n moment, and Δt represents the time step size.

[0113] The advantage of the backward Euler method lies in its good stability, especially effective when dealing with large temperature gradients.

[0114] According to the heat flux coupling equation, the backward Euler method is used to discretize the time derivative of the temperature field, obtaining:

[0115]

[0116] After rearrangement, we get:

[0117] KT n+1 = F + MT n

[0118] Among them, K represents the stiffness matrix, which contains the stiffness matrices of all small elements. M represents the fluid coupling matrix, which contains the fluid coupling terms of all small elements. T n+1 represents the temperature vector at the current time step t n+1 and contains the temperatures of all nodes. F represents the load vector, which contains the influence of the heat source term.

[0119] Through time discretization, the heat flux coupling equation is transformed into a system of linear algebraic equations. To efficiently solve this system of equations, the conjugate gradient method is used, specifically including:

[0120] Initialization: The initial temperature field T 0 is set to the initial condition, usually a uniform temperature field. The initial residual r 0= F - KT 0 。Initial search direction p 0 = r 0 。

[0121] Iterative calculation: In each iteration, calculate the step size α k :

[0122]

[0123] where r k represents the residual vector at the k-th iteration, and p k represents the search direction at the k-th iteration.

[0124] Update the temperature field T k+1 :

[0125] T k+1 = T k + α k p k

[0126] where T k+1 represents the updated temperature field, and T k represents the temperature field of the previous iteration.

[0127] Update the residual r k+1 :

[0128] r k+1 = r k - α k Kp k

[0129] where r k+1 represents the residual vector at the (k + 1)-th iteration.

[0130] Update the search direction p k+1 :

[0131] p k+1 = r k+1 + β k p k

[0132] where:

[0133]

[0134] Termination condition: When the residual r k is small enough or the number of iterations reaches the maximum value, stop the iteration.

[0135] Through the above steps, the coupled heat - flow equation is discretized based on the finite - element method, and the implicit time - discretization method (backward Euler method) and conjugate - gradient method are used to solve for the temperature field T(x, t) of the boiler at different time steps, which can more accurately describe the dynamic changes of the boiler temperature field.

[0136] In step three, the hydrodynamics equation is expressed as

[0137]

[0138] where v(x, t) represents the fluid velocity field, describing the distribution of the fluid velocity in space x and time t; ρ represents the fluid density; p represents the fluid pressure; μ represents the fluid viscosity; γ represents the influence coefficient of temperature on fluid flow velocity.

[0139] Specifically, calculating the flow velocity field inside the boiler includes discretizing the spatial domain of the boiler. Based on the geometric shape of the boiler components, it is divided into multiple finite elements, and each element is connected by nodes to form a discretized grid.

[0140] Based on the physical characteristics of the boiler, the finite - element method is used as the numerical solution method for the hydrodynamics equation to convert the fluid flow velocity field into the flow velocity values at discrete points and calculate at each node.

[0141] Set the boundary conditions of the flow velocity field. The boundary conditions of the flow velocity field include the velocity boundary condition at the fluid inlet, the temperature boundary condition at the fluid inlet, and the fluid pressure boundary condition at the outlet. Among them, the velocity and temperature at the fluid inlet reflect the initial working state of the boiler.

[0142] According to the discretized grid structure, construct the hydrodynamics equation for each unit to obtain the flow velocity field distribution at each node.

[0143] Through the time - domain analysis of the finite - element method, perform iterative calculations on the flow velocity field. In each time step, update the fluid velocity field according to the current temperature field and fluid characteristics until the convergence condition is met to obtain the dynamic flow velocity field of the boiler during the entire working cycle.

[0144] Specifically, under the conditions of steady - state or unsteady - state flow, temperature changes will affect the density and flow velocity of the fluid, thereby affecting the movement of the fluid. Specifically, an increase in temperature usually leads to a decrease in the fluid density, resulting in an increase in the buoyancy effect. The influence coefficient of temperature on fluid flow velocity is expressed as:

[0145]

[0146] where represents density, c p represents the specific heat capacity of the fluid, and α T represents the thermal expansion coefficient of the fluid.

[0147] In step four, the damage degree equation is expressed as

[0148]

[0149] where D(t) represents the damage condition of the boiler component at time t; σ(t) represents the stress borne at time t; σ max represents the maximum bearing stress of the material; T max represents the highest working temperature of the material; v max represents the maximum flow rate; n, m, p respectively represent the sensitivity indices of stress, temperature and flow rate to the material damage.

[0150] It should be noted that during the actual operation of the boiler, the material will experience periodic load changes, and such load changes have a direct impact on the fatigue damage of the material. Fatigue test is an effective method to obtain the influence of stress on material damage. Using the standard fatigue test method, the material is placed under different stress amplitudes for multiple loadings and unloadings to simulate the stress change situation of the boiler component during actual operation. Test the fatigue life N of the material (i.e., the rupture or failure time of the material under different stress levels) and the deformation amount of the material under different stress amplitudes.

[0151] Design several different stress amplitudes σ1, σ2, …, σ k , and test the fatigue lives {N1, N2, …, N k} of the material under these stress amplitudes.

[0152] According to the test results, fit the SN curve, that is, the relationship between the fatigue life N of the material and the stress amplitude σ:

[0153] N = C·σ -n

[0154] where C is a constant and n is the stress sensitivity index.

[0155] Through the SN curve fitted by the above experiments, the stress sensitivity index n can be obtained. Calculate the sensitivity index n through the experimental data under different stress levels. For example, apply the regression analysis method to fit n through the fatigue life data under different stress amplitudes.

[0156] Temperature is another important factor affecting the performance of boiler materials. The performance of materials often changes significantly in high-temperature environments, so the influence of temperature on materials needs to be quantified through experiments. To obtain the temperature sensitivity index m, operate according to the following steps:

[0157] High-temperature creep is a common phenomenon during boiler operation, especially in the high-temperature parts of the boiler. The long-term high-temperature load borne by the material may cause creep damage. Creep tests can effectively measure the impact of temperature on material damage.

[0158] Apply a constant stress in a high-temperature environment and test the creep behavior of the material by measuring its deformation. Creep tests are usually carried out in common high-temperature environments of boilers, such as testing the creep rate of the material under high-temperature conditions of 300°C, 500°C, 700°C, etc.

[0159] Conduct creep tests at multiple temperature points (e.g., 300°C, 500°C, 700°C) and record the deformation rate of the material. 。

[0160] Use a formula to fit the relationship between temperature and creep rate:

[0161]

[0162] where A is a constant, T is the temperature, T max is the maximum working temperature of the material, and m is the temperature sensitivity index.

[0163] Fit the relationship between temperature and creep rate through creep tests to obtain the temperature sensitivity index m. Calculate the impact of temperature on material damage based on creep test data at different temperatures and fit the temperature sensitivity index m.

[0164] Excessively high flow velocity may cause wear on the surface of the material, especially in parts such as the steam pipes and heat exchangers of the boiler. Fluid wear tests are used to evaluate the impact of flow velocity on material damage by simulating the fluid flow inside the boiler.

[0165] Select several typical flow velocities (e.g., 1 m / s, 2 m / s, 3 m / s, etc.) and conduct wear tests at these flow velocities. At each flow velocity, test the wear depth or mass loss of the material. Fit the relationship between flow velocity and damage based on the wear data:

[0166]

[0167] where B is a constant, v is the flow velocity, v max is the maximum flow velocity, and p is the flow velocity sensitivity index.

[0168] Analyze the impact of flow velocity on material damage through flow velocity test data and fit the flow velocity sensitivity index p.

[0169] In step five, determining the damage risk level of boiler components includes: when D(t) is less than or equal to the first damage threshold, it is determined as a low risk level; when D(t) is greater than the first damage threshold and less than the second damage threshold, it is determined as a medium risk level; when D(t) is greater than or equal to the second damage threshold, it is determined as a high risk level.

[0170] When it is determined as a low risk level, maintain the operating parameters of the current boiler.

[0171] When it is determined as a medium risk level, optimize the discretized grid, increase the grid density; update the temperature field, flow velocity field and damage degree equation using the encrypted discretized grid; obtain the damage degree distribution under grid encryption, identify abnormal grids, and adjust the operating parameters of the boiler components within the abnormal grids;

[0172] It should be noted that for local grid optimization of the discretized grid, an adaptive grid generation algorithm is adopted to identify the high gradient regions of the temperature field, flow velocity field and stress field, and encrypt the grids for these regions. Based on the encrypted grid, recalculate the temperature field, flow velocity field and damage degree equation. Specifically, based on the spatial resolution of the encrypted grid, update the temperature, flow velocity and damage degree values of each node to ensure the calculation accuracy. According to the optimized damage degree distribution, identify abnormal grids, where the abnormal grids are defined as regions where the damage degree exceeds the preset threshold. By comparing with the temperature, flow velocity and stress field data, determine the region with the most serious damage. For the region where the identified abnormal grids are located, adjust the operating parameters of the boiler components. The adjustment contents include optimizing the fluid flow velocity, temperature control and pressure adjustment, etc., to reduce or avoid potential component damage and ensure the safe operation of the boiler.

[0173] When it is determined as a high risk level, according to the damage degree distribution, determine the area that needs to be shut down, and perform local shutdown operations through a zoning control strategy; within the shutdown area, use acoustic emission detection technology to monitor the wear of boiler components and identify the wear locations; at the same time, use infrared thermal imaging technology to monitor the temperature anomalies in the combustion area, heat exchange area and cooling area; and evaluate the dynamic state of boiler components through vibration monitoring technology; according to the detection results, identify the locations with wear or explosion-proof hidden dangers; spray wear-resistant coatings or perform welding repairs on the wear locations, reinforce and seal the explosion-proof hidden danger locations, and adjust the fluid flow velocity, temperature control and pressure parameters; after the repair is completed, restart the boiler.

[0174] Specifically, firstly, by analyzing the distribution of damage degree, determine the area in the boiler that needs to be shut down. This step compares the damage degree value of each area of the boiler with the preset risk threshold. Through damage degree analysis, identify areas where the damage degree exceeds the high-risk threshold, and combine multiple physical field data such as heat flow and stress to use a zoning control strategy for local shutdown. The specific method is: for each boiler component (such as heat exchangers, burners, pipelines, etc.), spatial discretization is performed according to changes in parameters such as damage degree, temperature, and pressure, priority shutdown is performed according to the degree of damage, and the operation process is optimized. During shutdown, gradually reduce the fluid flow rate, temperature, and pressure in the boiler to reduce the mechanical load and prevent further damage.

[0175] In the shutdown area, acoustic emission detection technology is first used to monitor the wear of boiler components. Acoustic emission sensors are installed in key locations of boiler components (such as heat exchangers, burners, etc.). By collecting weak sound wave signals on the surface of boiler components in real time, cracks, wear or deformation on the surface or inside of the material can be identified. By analyzing the frequency, amplitude and waveform of the sound wave signal, the location and degree of wear can be accurately determined. The analysis of acoustic emission signals can be combined with damage degree data to accurately locate the worn parts and provide a basis for subsequent repair operations.

[0176] At the same time, infrared thermal imaging technology is used to monitor temperature anomalies in the combustion area, heat exchange area and cooling area of the boiler. The surface temperature distribution of boiler components is an important indicator for monitoring the operating status of the boiler. By installing infrared thermal imaging equipment at various key locations of the boiler (such as burners, heat exchangers, pipelines, etc.), the temperature changes on the surface of the boiler are monitored in real time. By capturing thermal radiation and generating temperature images of boiler components, the infrared thermal imager can clearly show possible temperature anomaly areas, such as local overheating or insufficient cooling. These temperature anomaly areas are usually related to equipment wear, corrosion, blockage and other problems, so infrared thermal imaging can effectively detect potential risks in boiler operation.

[0177] Vibration monitoring technology is used to evaluate the dynamic state of boiler components. Vibration sensors are installed at key parts of the boiler to collect vibration signals of boiler components in real time. The frequency and amplitude of the vibration signal reflect the dynamic characteristics of boiler components, such as whether resonance, looseness or deformation occurs during operation. In particular, abnormal vibration under high temperature and high pressure environments is often closely related to wear, cracks or structural damage. Based on the analysis of vibration signals and combined with other test data, the damage state of boiler components can be identified, and the location of wear or explosion hazards can be further confirmed.

[0178] After identifying the worn or explosion-proof hazard areas based on the results of the above detection techniques, the repair process is entered. For the worn areas, wear-resistant coating spraying or welding repair is adopted. The specific operations include: First, clean the surface of the worn area to remove impurities such as rust and oxides. Then, select an appropriate wear-resistant coating material (such as ceramic coating, alloy coating, etc.) according to the degree of wear, and use spraying technology to evenly coat the wear-resistant coating on the worn area. For severely worn components, welding repair is adopted. First, cut or clean the damaged area, and then use the corresponding welding technology to repair the damaged area to ensure that the component restores its original strength and durability.

[0179] For the explosion-proof hazard areas, measures such as support reinforcement and sealing treatment are taken for repair. Support reinforcement usually enhances its bearing capacity by installing support frames or adding reinforcing ribs outside the boiler components (such as pipes, furnace walls, etc.). Sealing treatment is to use materials such as explosion-proof sealant and high-temperature resistant gaskets to seal the high-pressure and high-temperature areas of the boiler to prevent leakage or explosion accidents. Especially in the high-risk areas of the boiler (such as the combustion area), reinforcement and sealing treatment are particularly important, which can effectively improve the safety and stability of the boiler components.

[0180] After completing the wear repair and explosion-proof hazard treatment, adjust the fluid flow rate, temperature control, and pressure parameters of the boiler according to the working state of the repaired area. After the repair is completed, first conduct a low-power test run to gradually restore the normal operating state of the boiler. During the operation, closely monitor the parameters such as temperature, pressure, and flow rate of the repaired area, and adjust the operating parameters of the boiler according to the monitoring data to ensure its operation in a safe and stable state.

[0181] After the repair is completed and the parameters are adjusted, restart the boiler. When restarting, adopt a method of gradually increasing the temperature and pressure to avoid causing new damage to the boiler components due to excessive pressure and temperature difference. After the boiler resumes normal operation, continuously monitor various operating parameters to ensure the safe and stable operation of the boiler.

[0182] Example 2, an embodiment of the present invention, provides a boiler anti-wear and explosion-proof detection and early warning system, including:

[0183] A data acquisition module for arranging sensors to collect boiler operation data;

[0184] A temperature analysis module for calculating the temperature field inside the boiler through the heat flow coupling equation;

[0185] A flow rate analysis module for calculating the flow rate field inside the boiler using the fluid mechanics equation;

[0186] A damage analysis module for constructing a damage degree equation based on the temperature field and flow rate field combined with the stress field distribution of the boiler components in the working environment to evaluate the damage condition of the boiler components;

[0187] An early warning adjustment module, which is used to judge the damage risk level of boiler components based on the calculation result of the damage degree equation, execute risk early warning operations, and optimize and adjust the operating parameters of the boiler.

[0188] Example 3, an embodiment of the present invention, which is different from the previous two embodiments in that:

[0189] If the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes contributions to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., which can store program codes.

[0190] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0191] More specific examples (nonexhaustive list) of computer-readable media include the following: electrical connection parts (electronic devices) having one or more wirings, portable computer disk cartridges (magnetic devices), random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROMs). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0192] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used to implement discrete logic circuits representing logic gate circuits for implementing logic functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0193] Example 4, an embodiment of the present invention, provides a method for detecting, preventing wear and explosion, and giving early warning of a boiler. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0194] The environmental conditions of the experiment are set as follows: The fluid in the boiler simulation area is water vapor, the average working temperature is 500 °C, and the pressure is set at 30 MPa. The boiler components are made of high-temperature resistant alloy steel, and the maximum bearing stress is 400 MPa. The flow velocity range is 5 - 10 m / s, and the temperature fluctuates between 300 °C and 600 °C. Sensors are arranged inside the boiler to collect temperature, flow velocity, and pressure information in real time. The traditional method relies on simplified thermodynamic formulas and empirical models, only considering temperature and pressure data, while the method of the present invention conducts a comprehensive damage assessment through numerical calculations of heat flow coupling equations and fluid mechanics equations, combined with the damage degree equation.

[0195] In the experiment, first, risk assessments are carried out using the traditional method and the method of the present invention respectively under high load (70%), medium load (50%), and low load (30%). The traditional method evaluates the damage of boiler components through temperature and fluid pressure data, combined with empirical formulas. The specific steps include: collecting sensor data, using empirical formulas to estimate the temperature field and flow velocity field, calculating the damage degree according to the simplified evaluation formula, and finally judging the risk level. The method of the present invention first collects real-time data of the boiler through sensors, calculates the temperature field and flow velocity field inside the boiler using heat flow coupling equations and fluid mechanics equations, discretizes each node through the finite element method, evaluates the damage of each node combined with the damage degree equation, and finally judges the risk level according to the calculation results and executes the early warning operation to adjust the boiler operation parameters. The experimental results are shown in Table 1.

[0196] Table 1 Comparison table of experimental results

[0197]

[0198] Traditional methods usually rely on simplified physical models and empirical formulas. Most of these methods ignore the coupling effects among the complex temperature field, flow velocity field, and stress field inside the boiler. The calculation processes of traditional methods often simplify or calculate these factors independently, resulting in the inability to comprehensively and accurately reflect the true damage conditions of boiler components under different working conditions. Especially under high-load and low-load conditions, the hydrodynamic changes inside the boiler are relatively complex, and traditional methods fail to fully capture this change, leading to deviations in the evaluation results.

[0199] In contrast, the present invention calculates jointly through heat-fluid coupling equations and fluid mechanics equations, taking into account the multi-dimensional coupling effects of temperature, flow velocity, and stress. This comprehensive calculation method can reflect the actual working environment inside the boiler, thus providing a more accurate risk assessment under complex working conditions such as high load and low load. Specifically, the method of the present invention adopts more refined numerical calculation and discretization techniques, which can perform dynamic iterative calculations on the temperature field and flow velocity field within multiple time steps, accurately capture the temperature fluctuations, fluid flow, and stress distribution of boiler components, and avoid the errors that may be brought by the simplified processing of traditional methods.

[0200] In addition, the present invention introduces a damage degree equation based on the coupling analysis of the temperature field and flow velocity field, quantifying the influence of temperature, flow velocity, and stress on the damage of boiler components into a comprehensive evaluation index, thereby realizing accurate risk judgment during boiler operation. The innovation of this method lies in that it not only synthesizes the influences of multiple physical fields but also optimizes and adjusts the boiler operation parameters according to the actual calculation results, effectively reducing the damage risk of boiler components and ensuring the safe and efficient operation of the boiler.

[0201] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A boiler anti-wear and explosion-proof detection and early warning method, characterized in that Including: Arrange sensors to collect boiler operation data; Calculate the temperature field inside the boiler through the heat-fluid coupling equation; Calculate the flow velocity field inside the boiler using the fluid mechanics equation; Based on the temperature field and the flow velocity field, combine the stress field distribution of boiler components in the working environment, construct a damage degree equation, and evaluate the damage status of boiler components; Based on the calculation results of the damage degree equation, judge the damage risk level of boiler components, perform risk warning operations, and adjust the operation parameters of the boiler; The calculation of the temperature field inside the boiler includes discretizing the spatial domain of the boiler, dividing it into multiple finite elements based on the geometric shape of the boiler components, and each element is connected by nodes to form a discretized grid; Based on the physical characteristics of the boiler, use the finite element method as the numerical solution method of the heat-fluid coupling equation, convert the continuous temperature field into temperature values at discrete points, and calculate at each node; Set the temperature field boundary conditions, and the temperature field boundary conditions include heat source boundary conditions, convective heat transfer boundary conditions, and initial temperature conditions; According to the discretized grid structure, construct the heat-fluid coupling equation for each element, and perform iterative calculations through the temperature distribution of the nodes to solve the temperature value of each node at each time step; Through the time-domain analysis of the finite element method, perform iterative calculations on the temperature field. Within each time step, update the temperature value of each node according to the temperature distribution information of the current time step to obtain the dynamic temperature field of the boiler during the entire working cycle; The heat-fluid coupling equation is expressed as Among them, T(x,t) represents the temperature at a certain position x and time t inside the boiler; α represents the thermal conductivity coefficient, which describes the heat conduction characteristics of the material; represents the second-order gradient of the temperature field, which describes the change of temperature in space; β represents the coefficient of coupling between fluid flow and temperature field, which characterizes the influence of fluid flow on heat conduction; v(x,t) represents the velocity field of the fluid; The damage degree equation is expressed as Among them, D(t) represents the damage condition of the boiler component at time t; σ(t) represents the stress borne at time t; σ max represents the maximum bearing stress of the material; T max represents the highest working temperature of the material; v max represents the maximum flow rate; n, m, p respectively represent the sensitivity indices of stress, temperature and flow rate to material damage; The thermal conductivity coefficient α is derived by combining the local heat source model and the temperature difference of the temperature field, and is expressed as: Where, α(x,t) is the thermal conductivity coefficient at a certain position x and time t inside the boiler, representing the heat conduction ability of this position, and ΔT(x,t) is the temperature difference at a certain position x and time t inside the boiler, representing the difference between the actual temperature and the target temperature at this position; Further optimize the thermal conductivity coefficient, introduce the least squares method, and adjust the thermal conductivity coefficient by minimizing the error between the temperature field inside the boiler and the target temperature field, which is expressed as: Among them, Error T is the temperature field error, representing the total error between the target temperature field and the actual temperature field, T target (x, t) is the target temperature field, preset based on boiler design or experimental data, and T(x, t) is the actual temperature field; By minimizing the error, the final thermal conductivity coefficient α(x,t) is obtained, which is expressed as: α opt (x,t) = α0 + k·(T target (x,t) - T(x,t)) where α opt (x, t) is the optimized thermal conductivity, α0 is the initial thermal conductivity, and k is the learning rate; The coefficient β of the coupling between fluid flow and temperature field reflects the coupling strength between fluid flow velocity and temperature field, and is expressed as: Where, β(x,t) is the coupling coefficient at a certain position x and time t inside the boiler, and ΔT(x,t) is the temperature difference, representing the difference between the actual temperature and the target temperature at this position; Use the particle swarm optimization algorithm to optimize the coefficient β of the coupling between fluid flow and temperature field to obtain the optimal coupling effect, which is expressed as: where Error β is the coupling coefficient error, and βtarget(x,t) is the target coupling coefficient; Through the optimization process, the following optimal coupling coefficient is finally obtained, which is expressed as:

2. The boiler abrasion and explosion prevention detection and early warning method according to claim 1, characterized in that: The fluid mechanics equation is expressed as Among them, \(v(x,t)\) represents the fluid velocity field, describing the distribution of the fluid velocity in space \(x\) and time \(t\); \(\rho\) represents the density of the fluid; \(p\) represents the pressure of the fluid; \(\mu\) represents the viscosity of the fluid; \(\gamma\) represents the influence coefficient of temperature on the fluid flow rate.

3. The method for boiler abrasion and explosion prevention detection and early warning according to claim 2, characterized in that: The calculation of the flow velocity field inside the boiler includes discretizing the spatial domain of the boiler, dividing it into multiple finite elements based on the geometric shape of the boiler components, and each element is connected by nodes to form a discretized grid; Based on the physical characteristics of the boiler, use the finite element method as the numerical solution method of the fluid mechanics equation, convert the fluid flow velocity field into flow velocity values at discrete points, and calculate at each node; Set the boundary conditions of the flow velocity field, where the boundary conditions of the flow velocity field include the velocity boundary condition of the fluid inlet, the temperature boundary condition of the fluid inlet, and the fluid pressure boundary condition of the outlet; Construct the hydrodynamic equations for each unit according to the discretized grid structure to obtain the distribution of the flow velocity field at each node; Through the time-domain analysis of the finite element method, perform iterative calculations on the flow velocity field. Within each time step, update the fluid velocity field according to the current temperature field and fluid characteristics until the convergence condition is met, and obtain the dynamic flow velocity field of the boiler during the entire working cycle.

4. The boiler abrasion and explosion prevention detection and early warning method according to claim 3, characterized in that: The determination of the damage risk level of the boiler components includes that when D(t) is less than or equal to the first damage threshold, it is judged as a low risk level; when D(t) is greater than the first damage threshold and less than the second damage threshold, it is judged as a medium risk level; when D(t) is greater than or equal to the second damage threshold, it is judged as a high risk level.

5. The boiler abrasion and explosion prevention detection and early warning method according to claim 4, characterized in that: When judged as a low risk level, maintain the current operating parameters of the boiler; When judged as a medium risk level, optimize the discretized grid and increase the grid density; Update the temperature field, flow velocity field, and damage degree equation using the encrypted discretized grid; Obtain the damage degree distribution under grid encryption, determine the abnormal grids, and adjust the operating parameters of the boiler components within the abnormal grids; When judged as a high risk level, determine the area that needs to be shut down according to the damage degree distribution, and perform local shutdown operations through the zoning control strategy; Within the shutdown area, use acoustic emission detection technology to monitor the wear of the boiler components and identify the worn parts; at the same time, use infrared thermal imaging technology to monitor the temperature anomalies in the combustion zone, heat exchange zone, and cooling zone; and evaluate the dynamic state of the boiler components through vibration monitoring technology; According to the detection results, identify the parts with wear or explosion-proof hidden dangers; Spray a wear-resistant coating or perform welding repair on the worn parts, reinforce and seal the explosion-proof hidden danger parts, and adjust the fluid flow rate, temperature control, and pressure parameters; after the repair is completed, restart the boiler.

6. A boiler anti-wear and explosion-proof detection and early warning system, which applies an anti-wear and explosion-proof detection and early warning method as described in any one of claims 1 to 5, characterized in that, Including: A data acquisition module for arranging sensors to collect the operating data of the boiler; A temperature analysis module for calculating the temperature field inside the boiler through the heat-fluid coupling equation; A flow velocity analysis module for calculating the flow velocity field inside the boiler using the hydrodynamic equations; A damage analysis module for constructing a damage degree equation based on the temperature field and flow velocity field combined with the stress field distribution of the boiler components in the working environment to evaluate the damage condition of the boiler components; and, An early warning adjustment module for judging the damage risk level of the boiler components based on the calculation results of the damage degree equation, performing risk early warning operations, and optimizing and adjusting the operating parameters of the boiler.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the boiler anti-wear and explosion-proof detection and early warning method described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the boiler anti-wear and explosion-proof detection and early warning method described in any one of claims 1 to 5.

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