Method and system for predicting, regulating and controlling wall temperature of supercritical carbon dioxide boiler

Abstract

The invention discloses a supercritical carbon dioxide boiler wall temperature prediction regulation and control method and system. The method comprises the steps that a combustion side hearth three-dimensional model and a heating surface pipeline one-dimensional flow model are constructed, and two-way coupling is achieved; assuming the initial highest wall temperature of the heating surface, setting boundary conditions, performing hearth combustion simulation, solving the heat flux density of the heating surface, and calculating the heat flux density of the pipeline; assuming the initial parameters of the sCO2 working medium in the pipeline, and calculating the temperature and flow distribution of the sCO2 working medium; constructing a heat transfer deterioration risk function, and judging whether a risk point exists in the pipeline or not; and calculating the maximum temperature of the dirt surface of the outer wall of the pipeline, judging whether the difference value between the maximum temperature and the assumed initial maximum wall temperature exceeds a preset value or not, and if yes, updating the assumed value and performing iterative calculation. According to the method, the heat transfer deterioration risk caused by sCO2 nonlinear physical property change is identified, so that an overtemperature accident is prevented, and the safety of the boiler is improved; a bidirectional coupling model is adopted to optimize a regulation strategy, the wall temperature regulation precision and response speed are improved, the boiler operation efficiency is enhanced, and the energy loss is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of boiler wall temperature prediction and control, and specifically relates to a supercritical carbon dioxide boiler wall temperature prediction and control method and system. Background Art

[0002] Supercritical carbon dioxide (sCO2) Brayton cycle coal-fired power generation technology boasts advantages such as high efficiency, compactness, and flexibility, and is considered one of the most promising next-generation power generation technologies. However, due to the drastic changes in the physical properties of sCO2 near the pseudo-critical point, this can lead to localized heat transfer degradation within the tubes, increased flow instability, and concentrated thermal stresses. Furthermore, the high heat load design of the boiler makes it difficult to effectively transfer local heat, leading to wall overheating and seriously compromising safe boiler operation.

[0003] Accurate wall temperature prediction and rapid wall temperature control can help improve the economy and safety of supercritical carbon dioxide boilers. Traditional wall temperature control methods mostly rely on the heat transfer characteristics under steady-state conditions, and have a slow response speed to transient operating conditions. It is difficult to adjust the wall temperature in time, especially under non-steady-state conditions such as large load fluctuations or startup and shutdown, which can easily lead to overtemperature accidents. The control method often only focuses on the influence of a single factor (such as flow rate or heat transfer coefficient) on the wall temperature, and does not fully consider the coupling effect of multiple factors, such as flow instability, non-uniform heat flux distribution and thermal stress coupling effects, resulting in unsatisfactory control effects. In addition, the prediction of wall temperature mainly focuses on whether it is overheating, and does not take into account the deterioration of the invisible heat transfer of the working fluid in the tube due to the nonlinear physical properties of sCO2, and cannot identify potential overtemperature risks. Summary of the Invention

[0004] To address the shortcomings and drawbacks of existing technologies, the present invention aims to provide a method for predicting and controlling the wall temperature of supercritical CO2 boilers based on a working fluid-side pipeline flow model. This method addresses the issues mentioned in the background regarding the proneness of supercritical CO2 boiler heating surface overheating and the inability to identify potential overheating risks. This method enables bidirectional coupling between the working fluid and combustion sides, accurately predicting the temperature at any point in the heating surface pipe section. Furthermore, a risk function is introduced to comprehensively consider the impact of multiple factors on wall temperature prediction, enabling immediate identification of potential heat transfer deterioration risks caused by nonlinear changes in the physical properties of sCO2.

[0005] In a first aspect, the present invention provides a method for predicting and controlling the wall temperature of a supercritical carbon dioxide boiler, comprising:

[0006] Step 1: Construct a three-dimensional model of the combustion side furnace and a one-dimensional flow model of the heating surface pipe and perform bidirectional coupling;

[0007] Step 2: Assuming the initial maximum wall temperature of the heating surface and setting the corresponding boundary conditions, perform numerical simulation of furnace combustion to calculate the heat flux density of the heating surface, and calculate the heat flux density of the pipeline line based on the heat flux density of the heating surface;

[0008] Step 3: Assuming the initial parameters of the sCO2 working fluid in the heating surface pipeline, perform numerical simulation calculations based on the pipeline line heat flux density to obtain the temperature and flow distribution of the sCO2 working fluid in the heating surface pipeline;

[0009] Step 4: Construct a heat transfer deterioration risk function based on the pipeline heat flux density to determine whether there is a heat transfer deterioration risk point in the heated surface pipeline. If there is, use the sCO2 physical properties and flow rate at the heat transfer deterioration risk point as the initial calculation parameters and perform iterative calculations until there is no heat transfer deterioration risk point.

[0010] Step 5: Based on the temperature distribution of the sCO2 working fluid in the entire heated surface pipeline, the maximum sCO2 temperature in the heated surface pipeline is obtained, and the maximum temperature of the dirt surface on the outer wall of the heated surface pipeline is calculated; and it is determined whether the difference between the maximum temperature and the assumed initial maximum temperature of the heated surface is greater than the preset value. If it is greater than the preset value, the assumed initial maximum wall temperature of the heated surface is updated for iterative calculation, and steps 2-5 are repeated until the difference is less than the preset value.

[0011] Furthermore, the three-dimensional model of the combustion side furnace in step 1 at least includes a combustion chamber, a burner, a flue, a water-cooled wall, a superheater, and a reheater;

[0012] The construction of the one-dimensional flow model of the heated surface pipeline includes: according to the actual tube bundle distribution and geometry of the heated surface pipeline, the center line of each pipeline is extracted as the fluid flow path, and the pipeline network layout is represented by nodes and line segments.

[0013] Furthermore, the numerical simulation calculation for furnace combustion in step 2 includes:

[0014] The turbulence model uses the SST k-ω model to analyze the intense heat transfer phenomenon dominated by turbulent flow near the heating surface, the PDF probability density function model is used to simulate the strong non-uniform combustion in the furnace, the Lagrangian particle trajectory model is used to track the trajectory, velocity and reaction process of the pulverized coal particles, and the multi-step reaction kinetic model is used to simulate the volatile decomposition, gas phase combustion and fixed carbon combustion in the combustion process; the Monte Carlo model is used to simulate the gas radiation heat transfer; and the gray body model is used to calculate the particle radiation heat transfer.

[0015] Furthermore, the pipeline heat flux density Q in step 3 tube for:

[0016]

[0017] Among them, Q wall is the heat flux density of the heated surface; D outer D is the outer diameter of the heating surface pipe; inner is the inner diameter of the heating surface pipe, L is the length of the heating surface pipe, k materialk is the thermal conductivity of the pipe material on the heating surface; fouling is the thermal conductivity of the dirt layer, d fouling It is the thickness of the dirt layer outside the heating surface pipe.

[0018] Furthermore, the heat transfer deterioration risk function in step 4 is:

[0019]

[0020] Among them, φ risk is the risk function value. For any point in the heating surface pipeline, k b is the thermal conductivity of the working fluid, D inner is the inner diameter of the heating surface pipe, T crit is the temperature of the working fluid at the critical state point, T b is the working medium temperature in the heating surface pipe, u b is the working fluid flow rate, ρ b is the density of the working fluid, μ b is the dynamic viscosity of the working fluid, C p，b is the specific heat capacity of the working fluid.

[0021] Furthermore, in step 4, an optimal risk threshold is set, and a heat transfer deterioration risk value is solved based on the heat transfer deterioration risk function. If the heat transfer deterioration risk value is greater than the optimal risk threshold, it is determined that a heat transfer deterioration risk point exists.

[0022] Furthermore, the steps for setting the optimal risk threshold are as follows:

[0023] The sudden increase of local wall temperature and the sudden drop of convective heat transfer coefficient are used as heat transfer deterioration state data, and the heat transfer deterioration state data are classified;

[0024] Based on the logistic regression model, the risk function value is fitted to form a standardized risk function value, and the standardized risk function value and heat transfer deterioration state data are trained;

[0025] ROC curve analysis was used to evaluate the performance of the logistic regression model and determine the optimal risk threshold.

[0026] Furthermore, solving the heat transfer deterioration risk value based on the heat transfer deterioration risk function includes: obtaining the temperature, flow velocity and heat flux density values at each point along the working fluid flow direction in the one-dimensional flow model of the pipeline through a probe, querying the sCO2 physical properties at the corresponding temperature through the physical property database, and bringing them into the heat transfer deterioration risk function for calculation.

[0027] Furthermore, in step 5, updating the assumed initial maximum wall temperature of the heated surface includes:

[0028] T wall,max(i+1) =[T wall,max(i) +Tfouling,max(i) ] / 2

[0029] Among them, T wall，max(i+1) Assume that the initial maximum wall temperature of the heated surface for the i+1th iteration, T wall，max(i) Assume that the initial maximum wall temperature of the heated surface for the i-th iteration, T fouling，max(i) is the maximum temperature of the dirt surface on the outer wall of the heated surface pipe in the i-th iteration.

[0030] In another aspect, the present invention provides a supercritical carbon dioxide boiler wall temperature prediction and control system, comprising:

[0031] Bidirectional coupling model construction module: It is used to construct a three-dimensional model of the combustion side furnace and a one-dimensional flow model of the heating surface pipe and perform bidirectional coupling;

[0032] Pipeline heat flux calculation module: It is used to assume the initial maximum wall temperature of the heating surface and set the corresponding boundary conditions to perform numerical simulation of furnace combustion to solve the heat flux density of the heating surface, and calculate the pipeline heat flux density based on the heat flux density of the heating surface;

[0033] Working fluid temperature and flow distribution acquisition module: used to assume the initial parameters of the sCO2 working fluid in the heating surface pipeline, and perform numerical simulation calculations based on the pipeline line heat flux density to obtain the temperature and flow distribution of the sCO2 working fluid in the heating surface pipeline;

[0034] Heat transfer deterioration judgment module: This module is used to construct a heat transfer deterioration risk function based on the pipeline heat flux density to determine whether there is a heat transfer deterioration risk point in the heated surface pipeline. If there is, the sCO2 physical properties and flow rate of the heat transfer deterioration risk point are used as the initial calculation parameters for iterative calculation until there is no heat transfer deterioration risk point.

[0035] Control module: It is used to obtain the maximum sCO2 temperature in the heating surface pipeline based on the temperature distribution of the sCO2 working fluid in the entire heating surface pipeline, calculate the maximum surface temperature of the dirt on the outer wall of the heating surface pipeline; and determine whether the difference between the maximum temperature and the assumed initial maximum temperature of the heating surface is greater than the preset value. If it is greater than the preset value, the assumed initial maximum wall temperature of the heating surface is updated and iterative calculation is performed until the difference is less than the preset value.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] According to the technical solution of this scheme, the wall temperature of the supercritical carbon dioxide boiler is predicted and controlled based on the working fluid side pipeline flow model, which solves the problem in the prior art that the heating surface of the supercritical carbon dioxide boiler is prone to overheating and the potential overheating risk cannot be effectively identified. By accurately predicting the wall temperature of any position of the heating surface pipe section and introducing a risk function to comprehensively consider the impact of multiple factors on the wall temperature prediction, the present invention can timely identify the potential heat transfer deterioration risk caused by the nonlinear physical property changes of sCO2, thereby improving the safety of boiler operation. In addition, this scheme optimizes the boiler's control strategy through bidirectionally coupled working fluid side and combustion side models, improves the accuracy and response speed of wall temperature control, and is particularly capable of rapid adjustment under non-steady-state conditions such as load fluctuations, startup and shutdown, thereby preventing the occurrence of overheating accidents. It provides a reliable quantitative analysis for the economy and safety of the boiler, effectively improves the operating efficiency of the boiler, and reduces energy losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 is a flow chart of an embodiment of the present invention.

[0040] Figure 2 It is a schematic diagram of the arrangement of a supercritical carbon dioxide boiler according to an embodiment of the present invention.

[0041] Figure 3 It is a schematic diagram of the mesh division of the three-dimensional model of the furnace according to an embodiment of the present invention.

[0042] Figure 4 . is a schematic diagram of the heat flux density distribution on the water-cooled wall surface of an embodiment of the present invention.

[0043] Figure 5 . is a step-by-step schematic diagram of the water-cooled wall pipe temperature in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] To make the above-mentioned objects, features, and advantages of the present application more clearly understood, the specific embodiments of the present application are described in detail below with reference to the accompanying drawings. The following description sets forth many specific details to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar improvements without violating the scope of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0045] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can be changed in the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved. The division of units described in this application is a logical division. In actual implementation, other divisions may be used. For example, multiple units may be combined or integrated into another system, or some features may be ignored or not implemented. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through interfaces, and the indirect coupling or communication connection between units may be electrical or other similar forms, all of which are not limited in this application. Furthermore, the units or subunits described as separate components may or may not be physically separated, may or may not be physical units, or may be distributed across multiple circuit units. Some or all of these units may be selected based on actual needs to achieve the objectives of this application.

[0046] Example 1

[0047] like Figure 1 As shown, this embodiment provides a method for predicting and controlling the wall temperature of a supercritical carbon dioxide boiler, comprising:

[0048] Step 1: Construct a three-dimensional model of the combustion side furnace and a one-dimensional flow model of the heating surface pipe and perform bidirectional coupling;

[0049] In this embodiment, a 300kW supercritical carbon dioxide (sCO2) boiler is taken as an example. Figure 2 The figure shows the layout of the boiler furnace. Figure 3 The figure shows the mesh division of the furnace 3D model.

[0050] According to the design plan of the supercritical carbon dioxide boiler, the overall shape and geometric dimensions of the boiler are obtained for three-dimensional modeling. The interior of the furnace includes at least a combustion chamber, burner, flue, water-cooled wall, superheater, reheater, etc.

[0051] For the simplification of the water-cooled wall, the tube bundle structure is ignored and is equivalent to a continuous plane. The plane is divided into multiple control sections along the direction of flue gas travel. The temperature and heat flux density are locally uniform within the control section, but significant differences are allowed between different control sections.

[0052] For the superheater and reheater, they are simplified into a two-dimensional heating plane, where the x direction represents the flue gas flow direction, and the y direction represents the height along the tube bundle. Different tube rows can be represented by continuous surface segments, and each surface segment represents the heating center of a row of tubes.

[0053] A one-dimensional flow model for pipes is established, using nodes and line segments to represent the pipe network layout. Based on the actual pipe bundle distribution and geometry, the centerline of each pipe is extracted as the fluid flow path. If the pipe is straight, the centerline represents the pipe length; if the pipe is curved, a multi-segment polyline or spline curve is used to approximate the centerline.

[0054] The calculation of the one-dimensional flow model of the pipeline and the three-dimensional model of the combustion side furnace are carried out in COMSOL and FLUENT respectively. The coupling between the models and the data exchange between the two software are realized through MATLAB.

[0055] Step 2: Assuming the initial maximum wall temperature of the heating surface and setting the corresponding boundary conditions, perform numerical simulation of furnace combustion to calculate the heat flux density of the heating surface, and calculate the heat flux density of the pipeline line based on the heat flux density of the heating surface;

[0056] For the numerical simulation of furnace combustion, the turbulence model uses the SST k-ω model to analyze the intense heat transfer phenomenon dominated by turbulent flow near the heating surface, the PDF probability density function model is used to simulate the strong non-uniform combustion in the furnace, and the Lagrangian particle trajectory model is selected to track the trajectory, velocity and reaction process of the pulverized coal particles. The volatilization analysis, gas phase combustion and fixed carbon combustion in the combustion process are simulated by multi-step reaction kinetics, the Monte Carlo model simulates gas radiation heat transfer, and the gray body model is used to calculate the particle radiation heat transfer.

[0057] For the water-cooled wall, T wall，max(i) It refers to the maximum temperature of the entire wall. The temperature non-uniformity is achieved by customizing the exponential distribution function in the solver. The formula is:

[0058] T wall (z) = T wall,max(i) ·e -αz

[0059] Where T wall(z) is the surface temperature at height z, and α is the exponential attenuation coefficient, which reflects the decay rate of temperature along the height.

[0060] For superheaters and reheaters, T wall，max(i)It refers to the maximum temperature of the first row. The non-uniformity of temperature distribution can be defined by the configuration file to change the temperature distribution with height and number of rows. The temperature formula for any nth row is:

[0061] T wall，n =T wall,max(i) exp(-β·(n-1))·f(y)

[0062] β represents the temperature attenuation coefficient, reflecting the shielding effect between different tube rows, and f(y) is the temperature distribution function along the height direction.

[0063] Step 3: Assuming the initial parameters of the sCO2 working fluid in the heating surface pipeline, perform numerical simulation calculation based on the pipeline line heat flux density to obtain the temperature and flow distribution of the sCO2 working fluid in the heating surface pipeline; Figure 4 The heat flux density distribution of the boiler water wall is shown as Figure 5 Shown is the temperature distribution of the water-cooled wall tubes.

[0064] This process can be viewed as a one-dimensional heat conduction problem involving the film resistance of the dirt on the outer wall of the pipe, while the heat transfer temperature difference caused by the pipe wall thickness can be regarded as the internal film resistance. Taking into account the heat conduction effects of dirt and pipe wall, the heat transfer resistance can be expressed as:

[0065] R total =R fouling +R wall

[0066] where R fouling is the external membrane resistance caused by fouling, and the formula is:

[0067]

[0068] Among them, d fouling is the thickness of the dirt layer outside the pipeline, k fouling is the thermal conductivity of the dirt layer, and the calculation formula is:

[0069]

[0070] Among them, G is the flue gas flow rate, A is the heat exchange area, T g is the flue gas temperature, T wall is the wall temperature of the heated surface, and μ is the viscosity of the flue gas.

[0071] where R wall is the internal film resistance caused by the tube wall, and the formula is:

[0072]

[0073] Among them D outer is the outer diameter of the pipe, D inner is the inner diameter of the pipe, L is the length of the pipe, kmaterial is the thermal conductivity of the pipe.

[0074] For any non-isothermal circular tube, the line heat flux density can be derived from the surface heat flux density using the following formula:

[0075] Q tube =Q wall ·A outer =Q wall ·πD outer

[0076] where Q wall is the heat flux density of the heated surface, unit is W / m 2 , Q tube is the linear heat flux density, unit W / m, A outer is the outer wall area of the circular tube, in m.

[0077] Therefore, the heat flux density Q of the heated surface wall Calculate the pipeline heat flux density Q tube The specific formula is:

[0078]

[0079] The finite element method (FEM) is used to simulate the sCO2 working fluid in pipelines. Compared to the traditional finite volume method (FVM), it offers greater flexibility for complex geometries such as bends, diameter changes, and intersections within the pipeline, accurately accounting for the effects of these structures on flow and heat transfer. This is particularly true in areas of the flow boundary layer or with large temperature gradients. By using higher-order elements, more refined temperature and velocity distributions can be obtained, thereby improving the accuracy of the calculation results. The model's calculations are implemented using the Non-Isothermal Pipe Flow Module in COMSOL Multiphysics.

[0080] The Darcy friction factor f D It is implemented through user-defined functions, and the calculation formula is:

[0081]

[0082] ρ b is the density of the mainstream working fluid, ρ w is the density of the working fluid near the wall, μ b is the viscosity of the mainstream working fluid, μ w is the viscosity of the working fluid near the wall, u is the flow rate of the working fluid, D is the hydraulic diameter, i.e. the inner diameter of the pipe, q tube is the heat load on the working fluid, that is, the heat flux density.

[0083] Step 4: Construct a heat transfer deterioration risk function based on the pipeline heat flux density to determine whether there is a heat transfer deterioration risk point in the heated surface pipeline. If there is, use the sCO2 physical properties and flow rate at the heat transfer deterioration risk point as the initial calculation parameters and perform iterative calculations until there is no heat transfer deterioration risk point.

[0084] The determination of whether there is a heat transfer deterioration risk point in the pipeline is mainly achieved through the heat transfer deterioration risk function. The function construction process is as follows:

[0085] The deterioration of sCO2 heat transfer is mainly caused by thermal property mutation, boundary layer characteristic change or flow instability. Therefore, the pipeline heat flux density Q is selected. tube , thermal conductivity k b , tube inner diameter D inner , temperature difference (T crit -T b ), T crit is the temperature of the working fluid at the critical state point, T b is the working medium temperature and flow velocity u in the pipeline b , dynamic viscosity μ b and specific heat capacity C p，b as a calculation indicator.

[0086] Q tube Reflects the heat load per unit length of the pipeline, k b Describes the ability of a fluid to transfer heat, D inner Determines the flow characteristics of the fluid, (T crit -T b ) is the main driving force for heat exchange, u b Determines the Reynolds number Re and the turbulence intensity of the fluid, μ b Affects the flow resistance and boundary layer thickness of the fluid, C p，b Describes the ability of a fluid to absorb heat and cause a change in temperature.

[0087] The risk function is initially constructed as follows:

[0088]

[0089] Perform dimensional analysis on the above formula:

[0090]

[0091] The dimension of the risk function is M·T -2 , that is, the energy density form, so the kinetic energy density E0 is introduced to normalize the risk function and further characterize the influence of flow state on heat transfer deterioration. The expression of kinetic energy density E0 is:

[0092]

[0093] The heat transfer deterioration risk function is finally determined as:

[0094]

[0095] Based on thermal engineering test experiments and literature retrieval, a large amount of sCO2 tube convection heat transfer data was obtained. The local wall temperature surge and the convective heat transfer coefficient drop were used as two indicators to judge the deterioration of heat transfer, and the data was classified. The data was then subjected to regression analysis based on the risk function to determine the risk threshold φ when heat transfer deterioration occurs. risk = N. The specific implementation is achieved with the help of code, and the operation process is as follows:

[0096] The first step is to convert the data into a form with a mean of 0 and a standard deviation of 1 through standardization. The formula is as follows

[0097]

[0098] where φ risk,standardized is the standardized risk function value, φ risk is the risk function value, mean(φ risk ) is the average value of the risk function, and the calculation formula is as follows;

[0099]

[0100] Where M is the number of samples, φ risk,i is the risk function value of the i-th sample, std(φ risk ) is the standard deviation of the risk function value, and the calculation formula is as follows:

[0101]

[0102] In the second step, the logistic regression model is used to fit the standardized risk function value and heat transfer deterioration state data.

[0103] The third step is to use ROC curve analysis to evaluate the performance of the model and determine the optimal risk threshold N through the curve;

[0104] The probe is used to obtain the temperature, flow velocity and heat flux density values at each point along the working medium flow direction in the one-dimensional flow model of the pipeline. The physical properties of sCO2 at the temperature are queried through the physical property database and the heat transfer deterioration risk function is brought into the calculation. risk If it is greater than N, it is considered that there is a risk of heat transfer deterioration, and the temperature, pressure and flow rate at this point are used as initial parameters for iterative calculation until there is no risk point of heat transfer deterioration.

[0105] Step 5: Based on the temperature distribution of the sCO2 working fluid in the entire heated surface pipeline, the maximum sCO2 temperature in the heated surface pipeline is obtained, and the maximum temperature of the dirt surface on the outer wall of the heated surface pipeline is calculated; and it is determined whether the difference between the maximum temperature and the assumed initial maximum temperature of the heated surface is greater than the preset value. If it is greater than the preset value, the assumed initial maximum wall temperature of the heated surface is updated for iterative calculation, and steps 2-5 are repeated until the difference is less than the preset value.

[0106] Based on the temperature of the sCO2 working medium in the heating surface pipeline in step 4, the maximum temperature T of sCO2 in the output pipe sco2，max(i) , calculate the maximum temperature T of the dirt surface on the outer wall of the pipe fouling，max(i) ;

[0107] Among them, the maximum working fluid temperature T obtained by numerical simulation is sco2，max(i) Calculate the maximum temperature T of the dirt surface on the outer wall of the pipe fouling，max(i) The method is the same as that in step 3 according to Q wall Calculate the pipeline heat load Q tube Similarly, considering the fouling and avoidance of the heat transfer membrane group, the calculation formula is as follows:

[0108]

[0109] Determine the pipe wall temperature T tube，wall(i) Assuming the initial temperature of the heated surface T wall(i) Is the difference between the two values greater than the preset value? If it is greater than the preset value, it means that the assumed temperature does not meet the actual heat transfer conditions and it is necessary to perform iterative calculations based on the latest pipe wall temperature. wall，max(i+1) =[T wall,max(i) +T fouling,max(i) ] / 2 is used as the maximum temperature of the initial heating surface in the numerical simulation of the combustion side for iterative calculation until the difference is less than the preset value, indicating that the thermal equilibrium condition has been achieved and the calculation process ends.

[0110] Example 2

[0111] This embodiment provides a supercritical carbon dioxide boiler wall temperature prediction and control system, including:

[0112] Bidirectional coupling model construction module: It is used to construct a three-dimensional model of the combustion side furnace and a one-dimensional flow model of the heating surface pipe and perform bidirectional coupling;

[0113] Pipeline heat flux calculation module: It is used to assume the initial maximum wall temperature of the heating surface and set the corresponding boundary conditions to perform numerical simulation of furnace combustion to solve the heat flux density of the heating surface, and calculate the pipeline heat flux density based on the heat flux density of the heating surface;

[0114] Working fluid temperature and flow distribution acquisition module: used to assume the initial parameters of the sCO2 working fluid in the heating surface pipeline, and perform numerical simulation calculations based on the pipeline line heat flux density to obtain the temperature and flow distribution of the sCO2 working fluid in the heating surface pipeline;

[0115] Heat transfer deterioration judgment module: This module is used to construct a heat transfer deterioration risk function based on the pipeline heat flux density to determine whether there is a heat transfer deterioration risk point in the heated surface pipeline. If there is, the sCO2 physical properties and flow rate of the heat transfer deterioration risk point are used as the initial calculation parameters for iterative calculation until there is no heat transfer deterioration risk point.

[0116] Control module: It is used to obtain the maximum sCO2 temperature in the heating surface pipeline based on the temperature distribution of the sCO2 working fluid in the entire heating surface pipeline, calculate the maximum surface temperature of the dirt on the outer wall of the heating surface pipeline; and determine whether the difference between the maximum temperature and the assumed initial maximum temperature of the heating surface is greater than the preset value. If it is greater than the preset value, the assumed initial maximum wall temperature of the heating surface is updated and iterative calculation is performed until the difference is less than the preset value.

[0117] The above is only a preferred specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed in this application should be covered by the scope of protection of the present application.

[0118] It should be understood that parts not elaborated in detail in this specification belong to the prior art.

[0119] It should be understood that the above description of the preferred embodiment is relatively detailed and cannot be regarded as limiting the scope of protection of the patent of the present invention. Under the guidance of the present invention, ordinary technicians in this field can also make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which all fall within the scope of protection of the present invention. The scope of protection requested by the present invention shall be based on the attached claims.

Claims

1. A method for predicting and controlling the wall temperature of a supercritical carbon dioxide boiler, characterized in that: include: Step 1: Construct a three-dimensional model of the combustion side furnace and a one-dimensional flow model of the heating surface pipe and perform bidirectional coupling; Step 2: Assuming the initial maximum wall temperature of the heating surface and setting the corresponding boundary conditions, perform numerical simulation of furnace combustion to calculate the heat flux density of the heating surface, and calculate the heat flux density of the pipeline line based on the heat flux density of the heating surface; Step 3: Assuming the initial parameters of the sCO2 working fluid in the heating surface pipeline, perform numerical simulation calculations based on the pipeline line heat flux density to obtain the temperature and flow distribution of the sCO2 working fluid in the heating surface pipeline; Step 4: Construct a heat transfer deterioration risk function based on the pipeline heat flux density to determine whether there is a heat transfer deterioration risk point in the heated surface pipeline. If there is, use the sCO2 physical properties and flow rate at the heat transfer deterioration risk point as the initial calculation parameters and perform iterative calculations until there is no heat transfer deterioration risk point. Step 5: Based on the temperature distribution of the sCO2 working fluid in the entire heated surface pipeline, the maximum sCO2 temperature in the heated surface pipeline is obtained, and the maximum temperature of the dirt surface on the outer wall of the heated surface pipeline is calculated; and it is determined whether the difference between the maximum temperature and the assumed initial maximum temperature of the heated surface is greater than the preset value. If it is greater than the preset value, the assumed initial maximum wall temperature of the heated surface is updated for iterative calculation, and steps 2-5 are repeated until the difference is less than the preset value.

2. A supercritical carbon dioxide boiler wall temperature prediction and control method according to claim 1, characterized in that: The three-dimensional model of the combustion side furnace in step 1 at least includes a combustion chamber, a burner, a flue, a water-cooled wall, a superheater, and a reheater; The construction of the one-dimensional flow model of the heated surface pipeline includes: according to the actual tube bundle distribution and geometry of the heated surface pipeline, the center line of each pipeline is extracted as the fluid flow path, and the pipeline network layout is represented by nodes and line segments.

3. The method for predicting and controlling the wall temperature of a supercritical carbon dioxide boiler according to claim 1, characterized in that: The numerical simulation calculation for furnace combustion in step 2 includes: The turbulence model uses the SST k-ω model to analyze the intense heat transfer phenomenon dominated by turbulent flow near the heating surface, the PDF probability density function model is used to simulate the strong non-uniform combustion in the furnace, the Lagrangian particle trajectory model is used to track the trajectory, velocity and reaction process of the pulverized coal particles, and the multi-step reaction kinetic model is used to simulate the volatile decomposition, gas phase combustion and fixed carbon combustion in the combustion process; the Monte Carlo model is used to simulate the gas radiation heat transfer; and the gray body model is used to calculate the particle radiation heat transfer.

4. The method for predicting and controlling the wall temperature of a supercritical carbon dioxide boiler according to claim 1, characterized in that: The pipeline heat flux density Q in step 3 tube for: Among them, Q wall is the heat flux density of the heated surface; D outer D is the outer diameter of the heating surface pipe; inner is the inner diameter of the heating surface pipe, L is the length of the heating surface pipe, k material k is the thermal conductivity of the pipe material on the heating surface; fouling is the thermal conductivity of the dirt layer, d fouling It is the thickness of the dirt layer outside the heating surface pipe.

5. A supercritical carbon dioxide boiler wall temperature prediction and control method according to claim 4, characterized in that: The heat transfer deterioration risk function in step 4 is: Among them, φ risk is the risk function value. For any point in the heating surface pipeline, k b is the thermal conductivity of the working fluid, D inner is the inner diameter of the heating surface pipe, T crit is the temperature of the working fluid at the critical state point, T b is the working medium temperature in the heating surface pipe, u b is the working fluid flow rate, ρ b is the density of the working fluid, μ b is the dynamic viscosity of the working fluid, C p，b is the specific heat capacity of the working fluid.

6. A supercritical carbon dioxide boiler wall temperature prediction and control method according to claim 5, characterized in that: In step 4, an optimal risk threshold is set, and a heat transfer deterioration risk value is solved based on the heat transfer deterioration risk function. If the heat transfer deterioration risk value is greater than the optimal risk threshold, it is determined that a heat transfer deterioration risk point exists.

7. A supercritical carbon dioxide boiler wall temperature prediction and control method according to claim 6, characterized in that: The steps to set the optimal risk threshold are as follows: The sudden increase of local wall temperature and the sudden drop of convective heat transfer coefficient are used as heat transfer deterioration state data, and the heat transfer deterioration state data are classified; Based on the logistic regression model, the risk function value is fitted to form a standardized risk function value, and the standardized risk function value and heat transfer deterioration state data are trained; ROC curve analysis was used to evaluate the performance of the logistic regression model and determine the optimal risk threshold.

8. The method for predicting and controlling the wall temperature of a supercritical carbon dioxide boiler according to claim 6, characterized in that: Solving the heat transfer deterioration risk value based on the heat transfer deterioration risk function includes: obtaining the temperature, flow velocity and heat flux density values at each point along the working medium flow direction in the one-dimensional flow model of the pipeline through a probe, querying the sCO2 physical properties at the corresponding temperature through the physical property database, and bringing them into the heat transfer deterioration risk function for calculation.

9. The method for predicting and controlling the wall temperature of a supercritical carbon dioxide boiler according to claim 6, characterized in that: In step 5, updating the assumed initial maximum wall temperature of the heated surface includes: T wall,max(i+1) =[T wall,max(i) +T fouling,max(i) ] / 2 Among them, T wall，max(i+1) Assume that the initial maximum wall temperature of the heated surface for the i+1th iteration, T wall，max(i) Assume that the initial maximum wall temperature of the heated surface for the i-th iteration, T fouling，max(i) is the maximum temperature of the dirt surface on the outer wall of the heated surface pipe in the i-th iteration.

10. A supercritical carbon dioxide boiler wall temperature prediction and control system, characterized in that: include: Bidirectional coupling model construction module: It is used to construct a three-dimensional model of the combustion side furnace and a one-dimensional flow model of the heating surface pipe and perform bidirectional coupling; Pipeline heat flux calculation module: It is used to assume the initial maximum wall temperature of the heating surface and set the corresponding boundary conditions to perform numerical simulation of furnace combustion to solve the heat flux density of the heating surface, and calculate the pipeline heat flux density based on the heat flux density of the heating surface; Working fluid temperature and flow distribution acquisition module: used to assume the initial parameters of the sCO2 working fluid in the heating surface pipeline, and perform numerical simulation calculations based on the pipeline line heat flux density to obtain the temperature and flow distribution of the sCO2 working fluid in the heating surface pipeline; Heat transfer deterioration judgment module: This module is used to construct a heat transfer deterioration risk function based on the pipeline heat flux density to determine whether there is a heat transfer deterioration risk point in the heated surface pipeline. If there is, the sCO2 physical properties and flow rate of the heat transfer deterioration risk point are used as the initial calculation parameters for iterative calculation until there is no heat transfer deterioration risk point. Control module: This module is used to obtain the maximum sCO2 temperature in the heating surface pipeline based on the temperature distribution of the sCO2 working fluid in the entire heating surface pipeline, calculate the maximum surface temperature of the dirt on the outer wall of the heating surface pipeline, and determine whether the difference between the maximum temperature and the assumed initial maximum temperature of the heating surface is greater than a preset value. If so, the assumed initial maximum wall temperature of the heating surface is updated and iterative calculation is performed until the difference is less than the preset value. The supercritical carbon dioxide boiler wall temperature prediction and control system based on the working medium side pipeline flow model is used to execute the steps in the supercritical carbon dioxide boiler wall temperature prediction and control method according to any one of claims 1 to 9.

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