Mathematical model construction method and device for coal-fired boiler

Abstract

The invention relates to the field of coal-fired boiler optimization, and discloses a mathematical model construction method and device for a coal-fired boiler. In order to solve the problem that existing coal-fired boiler operation simulation means are insufficient, the method comprises the steps that firstly, an automatic data acquisition system is utilized, and design parameters are obtained through an OCR technology, a data interface technology and an image recognition algorithm; based on the design parameters, constructing a physical model by using parametric modeling, intelligent assembly, multi-source data fusion and parallel computing technologies; and finally, establishing a mathematical model by combining the optimized basic conservation equation, turbulence model and the like through a self-adaptive grid division technology. The related device comprises an acquisition unit, a first construction unit and a second construction unit. In addition, the invention also provides a computer readable storage medium and electronic equipment. According to the invention, the operation state of the boiler can be accurately simulated, the abnormity can be timely checked, the system safety and economy are improved, and the system has significant practicability and innovativeness.

Description

Technical Field

[0001] The present invention belongs to the field of coal-fired boiler optimization, and in particular relates to a method and device for constructing a mathematical model of a coal-fired boiler. Background Art

[0002] As an abundant and widely used energy source, coal plays a crucial role in power generation. Coal-fired boilers, the core equipment for efficiently converting coal into thermal energy, are widely used in various coal-fired power plants. Their operating efficiency and environmental performance are directly related to the economic benefits and environmental responsibility of the power plant.

[0003] With the continued growth of energy demand and increasingly stringent environmental standards, improving the efficiency and environmental performance of coal-fired boilers has become a key research direction in the energy sector. For coal-fired power plants, accurate, real-time monitoring of the internal conditions of coal-fired boilers is crucial for ensuring efficient and safe operation. This not only helps optimize the combustion process and improve energy efficiency, but also effectively reduces pollutant emissions and mitigates negative environmental impacts.

[0004] However, current methods for monitoring the operating status of coal-fired boilers have significant flaws. Traditional monitoring relies primarily on a limited number of sensors to collect data. Due to limitations in sensor placement and quantity, these sensors cannot fully and accurately reflect the complex physical and chemical processes within the boiler. For example, in key areas within the furnace, such as near the burner and around the heating surface, sensors struggle to accurately measure the detailed distribution of parameters such as temperature, pressure, and flow rate, leading to a distorted understanding of the combustion and heat transfer processes.

[0005] Furthermore, traditional monitoring methods rely heavily on the operator's experience and judgment. However, this experience is often influenced by individual knowledge, operating habits, and the complexity of actual operating conditions, making it difficult to ensure accurate and consistent judgments. When faced with unexpected issues under varying operating conditions, judgments based on experience can lead to misjudgments or delayed resolution, impacting the boiler's stable operation and even causing safety incidents.

[0006] In terms of simulation technology, existing methods are unable to accurately simulate the complex phenomena within coal-fired boilers. For one thing, they lack in-depth consideration of multi-physics coupling processes, such as the interactions between chemical reactions, heat and mass transfer, and fluid flow during combustion. Furthermore, model parameter settings are often based on idealized assumptions or simple empirical formulas, which differ significantly from actual operating conditions. This leads to significant deviations between simulation results and actual conditions, and fails to provide reliable theoretical support for optimized boiler operation.

[0007] In summary, the existing coal-fired boiler operation monitoring and simulation methods are difficult to meet the current needs of efficient energy utilization and strict environmental protection requirements. There is an urgent need for a more accurate and efficient simulation method and related technologies to achieve comprehensive and accurate monitoring and analysis of the operating status of coal-fired boilers, and promote substantial progress in improving the efficiency and reducing environmental emissions of coal-fired boilers. Summary of the Invention

[0008] This invention aims to provide a method and apparatus for constructing a mathematical model of a coal-fired boiler, addressing the current inadequacy of existing methods for simulating coal-fired boiler operation. Through innovative technical details and optimized method steps, the accuracy and practicality of the mathematical model construction are significantly improved, enhancing the overall inventiveness of the invention.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] A method for constructing a mathematical model of a coal-fired boiler, comprising:

[0011] Obtain design parameters of coal-fired boilers;

[0012] constructing a physical model of the coal-fired boiler based on the design parameters;

[0013] A mathematical model of the coal-fired boiler is constructed based on the physical model.

[0014] A further improvement of the present invention is that the design parameters of the coal-fired boiler are obtained by using an automated data acquisition system, utilizing optical character recognition technology and data interface technology to extract data from the boiler's finalized manual and design drawings, and extracting coordinate information from the DCS sensor measurement point layout map through an image recognition algorithm.

[0015] A further improvement of the present invention is that the construction of the physical model of the coal-fired boiler based on the design parameters includes: performing three-dimensional modeling of the physical model of the coal-fired boiler based on modeling software, introducing parametric modeling technology into the modeling process, and using intelligent assembly technology to assemble components.

[0016] A further improvement of the present invention is that the construction of the physical model of the coal-fired boiler based on the design parameters also includes: obtaining a simulation data set based on the design parameters of the coal-fired boiler, and obtaining numerical simulation results of the coal-fired boiler mixture under different operating conditions, wherein the simulation data set is obtained using multi-source data fusion technology, and the numerical simulation results are obtained using parallel computing technology.

[0017] A further improvement of the present invention is that constructing a mathematical model of the coal-fired boiler based on the physical model includes: meshing the mathematical model of the coal-fired boiler, using adaptive meshing technology, and adjusting the mesh density according to the internal flow field and temperature field of the boiler.

[0018] A further improvement of the present invention is that the mathematical model of the coal-fired boiler constructed based on the physical model also includes: establishing a mathematical model of the coal-fired boiler based on basic conservation equations, turbulence model, discrete phase model, gas phase combustion model, coal char oxidation model, devolatilization model and radiation model, and optimizing and improving each model.

[0019] A further improvement of the present invention is that the turbulence model uses a realizable k-ε model, and the influence of complex flow boundary conditions in the boiler and the combustion process on turbulence is considered by introducing a correction coefficient.

[0020] A further improvement of the present invention is that the discrete phase model adopts a random trajectory model, and the particle motion trajectory calculation method is improved by considering the interaction between particles and the rebound characteristics of collision with the wall.

[0021] A device for constructing a mathematical model of a coal-fired boiler, comprising:

[0022] An acquisition unit, used for acquiring design parameters of a coal-fired boiler;

[0023] A first construction unit is configured to construct a physical model of the coal-fired boiler based on the design parameters;

[0024] The second construction unit is configured to construct a mathematical model of the coal-fired boiler based on the physical model.

[0025] A computer-readable storage medium includes a stored program, which implements the steps of the method for constructing a mathematical model of a coal-fired boiler when the program is executed by a processor.

[0026] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0027] The method and device for constructing a mathematical model of a coal-fired boiler provided by the present invention solve the problem of the lack of a better simulation method for the operation of the coal-fired boiler. The present invention constructs a mathematical model of the coal-fired boiler by referring to the design parameters of the coal-fired boiler. The digital model helps staff to promptly discover and troubleshoot abnormal conditions, such as local overheating, incomplete combustion of fuel, and other problems. By simulating and detecting the operating status of the boiler, the user-friendliness and operational safety of the entire system are improved.

[0028] Correspondingly, the mathematical model construction device and computer-readable storage medium for a coal-fired boiler provided in the embodiments of the present invention also have the above-mentioned technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific 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.

[0030] Figure 1 A schematic flow chart of a method for constructing a mathematical model of a coal-fired boiler provided by an embodiment of the present invention is shown;

[0031] Figure 2 A schematic block diagram showing the composition of a device for constructing a mathematical model of a coal-fired boiler provided by an embodiment of the present invention is shown;

[0032] Figure 3 A schematic block diagram showing the composition of an electronic device for constructing a mathematical model of a coal-fired boiler provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0033] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and description are to be considered as illustrative in nature and not restrictive.

[0034] In the description of the present invention, it is to be understood that when used in this specification and the appended claims, the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0035] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0036] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0037] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0038] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0039] Example 1

[0040] In a first aspect, the present invention provides a method for constructing a mathematical model of a coal-fired boiler, such as Figure 1 As shown, the method includes:

[0041] Step S101: Obtain design parameters of the coal-fired boiler:

[0042] Traditional methods for acquiring coal-fired boiler design parameters rely heavily on manual collection and organization, which is not only inefficient but also prone to data omissions and errors. This invention employs an automated data acquisition system, utilizing optical character recognition (OCR) and data interface technologies, to extract data directly from various design materials, including final boiler specifications, design drawings, and thermal calculation summary tables. For the DCS sensor point layout diagram, an image recognition algorithm accurately locates the measurement points and automatically extracts coordinate information, ensuring efficient and accurate acquisition of design parameters and laying a solid data foundation for subsequent model construction.

[0043] Step S102: Constructing a physical model of the coal-fired boiler based on the design parameters:

[0044] Step S102.1: Perform three-dimensional modeling on the physical model of the coal-fired boiler based on modeling software.

[0045] When constructing the physical model of the coal-fired boiler, parametric modeling technology was introduced within the 3D modeling process using modeling software. Using SolidWorks as an example, the boiler's design parameters, such as size, shape, and structural proportions, were input as variables into the parametric model. This allows the model to automatically update when design parameters change, eliminating the need to redraw the entire model, significantly improving modeling efficiency. Furthermore, during the model construction process, intelligent assembly technology was used to automatically assemble components based on their design relationships, reducing manual intervention and improving the accuracy and completeness of the model.

[0046] Step S102.2: Acquire a simulation data set based on the design parameters of the coal-fired boiler.

[0047] To obtain simulation data sets based on design parameters, multi-source data fusion technology is employed. This data is combined with historical boiler operation data, real-time monitoring data, and theoretical calculation data, and then deeply analyzed and integrated using data mining algorithms. For example, association rule mining algorithms can be used to identify potential relationships between parameters under different operating conditions, thereby obtaining a more comprehensive and accurate simulation data set.

[0048] Step S102.3: Obtain numerical simulation results of coal-fired boiler mixtures under different operating conditions.

[0049] In obtaining numerical simulation results of coal-fired boiler mixtures under different operating conditions, parallel computing technology is used, and multi-core processors or high-performance computing clusters are used to perform numerical simulation calculations on multiple operating conditions at the same time, which greatly shortens the simulation time and improves modeling efficiency.

[0050] Step S103: Constructing a mathematical model of the coal-fired boiler based on the physical model

[0051] Step S103.1: gridding the mathematical model of the coal-fired boiler to establish the mathematical model of the coal-fired boiler.

[0052] When constructing a mathematical model of a coal-fired boiler based on a physical model, adaptive meshing technology is introduced during meshing. The mesh density is automatically adjusted based on the complexity of the flow and temperature fields within the coal-fired boiler. In areas with drastic flow field changes or large temperature gradients, such as near the burner and on the heating surface, a finer mesh is used to improve simulation accuracy. In areas with relatively gentle flow and temperature changes, a sparser mesh is used to reduce computational effort and balance accuracy and efficiency.

[0053] Step S103.2: Establish a mathematical model of a coal-fired boiler based on basic conservation equations, turbulence model, discrete phase model, gas phase combustion model, char oxidation model, devolatilization model and radiation model.

[0054] When establishing the mathematical model of coal-fired boiler based on basic conservation equations, turbulence model, discrete phase model, gas phase combustion model, coal char oxidation model, devolatilization model and radiation model, each model is optimized and improved.

[0055] For the turbulence model (the realizable k-ε model is selected), by introducing a correction coefficient, the complex flow boundary conditions in the coal-fired boiler and the influence of the combustion process on turbulence are taken into account, so that the model can more accurately describe the turbulence characteristics.

[0056] In the discrete phase model (using the random trajectory model to simulate the discrete phase motion in the flow field in the Lagrangian coordinate system), the interaction between particles and the collision and rebound characteristics between particles and the wall are considered, and the idea of molecular dynamics simulation is applied to improve the calculation method of particle motion trajectory and improve the accuracy of discrete phase motion simulation.

[0057] When the gas-phase combustion model is combined with the finite rate chemical reaction model and the turbulent eddy dissipation model, a reaction progress variable is introduced to track the progress of the combustion reaction in real time. The parameters of the combustion model are dynamically adjusted according to the reaction progress to improve the accuracy of the combustion simulation.

[0058] For the coal char oxidation model (using an improved coal surface reaction mechanism to simulate the coal char oxidation process), the influence of the microstructural changes of the coal char on the oxidation reaction is considered, and the quantum chemical calculation method is used to accurately calculate the activation energy and reaction rate constant of the coal char surface reaction to optimize the coal char oxidation model.

[0059] In the devolatilization model (using the chemical permeation devolatilization (CPD) model as the devolatilization model of the simulated coal process), the parameters of the CPD model are optimized using a machine learning algorithm in combination with the coal pyrolysis experimental data to improve the accuracy of the simulation of the coal devolatilization process.

[0060] The radiation model takes into account that the intensity of radiation heat transfer varies in different areas of the furnace. It uses deep learning algorithms to study and analyze the radiation heat transfer data in the furnace, establish a more accurate radiation heat transfer model, and improve the accuracy of radiation heat transfer simulation.

[0061] Second, as Figure 2 As shown, an embodiment of the present invention further provides a device for constructing a mathematical model of a coal-fired boiler, comprising:

[0062] an acquisition unit, which uses the above-mentioned automated data acquisition system to acquire design parameters of the coal-fired boiler;

[0063] The first construction unit integrates parametric modeling technology, intelligent assembly technology, multi-source data fusion technology, and parallel computing technology to build a physical model of the coal-fired boiler based on the design parameters;

[0064] The second construction unit uses adaptive meshing technology and optimized models to establish a mathematical model of the coal-fired boiler.

[0065] An embodiment of the present invention further provides a computer-readable storage medium, which, when the stored program is executed by a processor, implements the steps of the above-mentioned method for constructing a mathematical model of a coal-fired boiler containing innovative technical details.

[0066] The embodiment of the present invention also provides an electronic device such as Figure 3As shown, the processor calls the program instructions in the memory to execute the steps of the above-mentioned improved method for constructing the mathematical model of the coal-fired boiler.

[0067] Through the above technical solution, the method and device for constructing a mathematical model of a coal-fired boiler provided by the present invention solve the problem of the lack of a better simulation method for the operation of the coal-fired boiler. The present invention constructs a mathematical model of the coal-fired boiler by referring to the design parameters of the coal-fired boiler. The digital model helps staff to promptly discover and troubleshoot abnormal conditions, such as local overheating, incomplete combustion of fuel, and other problems. By simulating and detecting the operating status of the boiler, the user-friendliness and operational safety of the entire system are improved.

[0068] Example 2

[0069] Taking a 600MW supercritical coal-fired boiler as an example, the specific implementation process of the present invention is described in detail.

[0070] In a first aspect, the present invention provides a method for constructing a mathematical model of a coal-fired boiler, the method comprising:

[0071] Step S101: Obtain design parameters of coal-fired boiler

[0072] Utilizing a specially developed automated data acquisition system integrated with advanced optical character recognition (OCR) technology, paper documents such as final boiler manuals and thermal calculation summary tables are scanned into electronic image format using a high-speed scanner. OCR technology automatically identifies the textual information within, such as the boiler's rated evaporation capacity, superheated steam temperature, pressure, and other key parameters, and converts it into editable text data.

[0073] For design drawings, data interface technology is used to connect with the database of design drawing software (such as AutoCAD). Through a specific interface program, geometric parameters such as the size, shape, and position relationship of each boiler component are directly extracted from the drawing database, ensuring the accuracy and completeness of the data and avoiding errors that may occur during manual measurement and reading.

[0074] A deep learning-based image recognition algorithm was applied to the DCS sensor point layout diagram. Trained with extensive image data of labeled measurement point locations, the algorithm accurately identifies measurement points within the diagram and automatically extracts their coordinates. This coordinate information is recorded in real time and integrated with other design parameters, providing the foundational data for subsequent, accurate simulation of boiler operating conditions.

[0075] Step S102: Constructing a physical model of the coal-fired boiler based on the design parameters

[0076] Step S102.1: Perform three-dimensional modeling of the physical model of the coal-fired boiler based on the modeling software: Solidworks modeling software is selected for three-dimensional modeling. The previously acquired boiler design parameters, such as the length, width and height of the furnace, the number and layout of the burners, the pipe diameter and length of the heating surface, etc., are input into the model parameter table of Solidworks in accordance with the parametric modeling method. When the boiler design needs to be modified, such as adjusting the heat exchange area of the heating surface, it is only necessary to modify the corresponding pipe diameter or pipe length parameters in the parameter table, and the model can be automatically updated and the modified three-dimensional model can be regenerated without redrawing the entire model, which greatly improves the modeling efficiency. During the model construction process, the intelligent assembly function of Solidworks is used to automatically complete the assembly of components according to the design relationship of the various components of the boiler, such as the connection method between the burner and the furnace, the assembly relationship between the heating surface and the pipeline, etc., through the set assembly constraints, thereby reducing errors in the manual assembly process and improving the accuracy and completeness of the model.

[0077] Step S102.2: Acquire a simulation data set based on the design parameters of the coal-fired boiler: collect the historical operating data of the boiler for the past 10 years, including data such as fuel quantity, steam flow, flue gas temperature and composition under different loads. At the same time, combine the current operating data obtained by the real-time monitoring system with the theoretical calculation data obtained based on boiler design principles and thermal calculation methods. Use the association rule mining algorithm (such as the Apriori algorithm) in the data mining algorithm to conduct an in-depth analysis of these multi-source data. For example, through mining, it is found that under high-load operating conditions, when the fuel quantity increases by 10%, the steam flow will increase by 8%-9% accordingly, and the flue gas temperature at the furnace outlet will increase by 15-20°C. In this way, the potential relationship between the parameters under different operating conditions is found, thereby obtaining a more comprehensive and accurate simulation data set.

[0078] Step S102.3: Obtain numerical simulation results of coal-fired boiler mixtures under different operating conditions: Utilize high-performance computing clusters and apply parallel computing technology. According to the actual operating conditions of the boiler, 15 different operating conditions such as high load, low load, and variable load are set. Numerical simulation calculations are performed on these 15 operating conditions simultaneously on multiple computing nodes in the computing cluster. Each computing node simulates the combustion process of the fuel, the flow and heat transfer process of the flue gas, and the movement of the gas-solid two-phase flow in the coal-fired boiler under one operating condition. Through parallel computing, simulation calculations that originally took several weeks to complete can be completed in just a few days, which greatly shortens the simulation time, improves modeling efficiency, and obtains accurate numerical simulation results under different operating conditions.

[0079] Step S103: Constructing a mathematical model of the coal-fired boiler based on the physical model

[0080] Step S103.1: Meshing the mathematical model of the coal-fired boiler to establish a mathematical model of the coal-fired boiler: The constructed physical model is meshed using Ansys Fluent software, employing adaptive meshing technology. In the area near the burner, due to the intense fuel combustion and extremely complex flow and temperature field variations, the meshing software automatically generates a fine mesh with a minimum mesh size of 0.01m to ensure accurate capture of the various physical phenomena in the combustion process. In the heating surface area, given the complexity of the heat exchange process and the large temperature gradients, a fine mesh is also used, with a mesh size between 0.02-0.05m. In areas away from the burner and heating surface, where flow and temperature field variations are relatively gradual, such as the boiler tail flue, a sparser mesh is used, with a mesh size of 0.1-0.2m. This effectively reduces the amount of computation while ensuring simulation accuracy, balancing computational accuracy and efficiency.

[0081] Step S103.2: Establish a mathematical model of a coal-fired boiler based on basic conservation equations, turbulence model, discrete phase model, gas phase combustion model, char oxidation model, devolatilization model, and radiation model

[0082] Turbulence Model Optimization: A realizable k-ε model was selected, and a correction factor was introduced based on the actual operating parameters and internal flow characteristics of the boiler. Through analysis of extensive experimental data and simulation results, a correction factor of 1.2 was determined. This correction factor accounts for the complex flow boundary conditions within coal-fired boilers, such as the interaction between the burner jet and the main flow in the furnace, as well as the impact of the combustion process on turbulence, enabling the model to more accurately describe turbulent characteristics.

[0083] Improved discrete phase model: A random trajectory model is used to simulate the motion of discrete phases (such as fly ash particles) in the flow field in a Lagrangian coordinate system. By taking into account the interactions between particles and the rebound characteristics of particle-wall collisions, and applying molecular dynamics simulation techniques, the calculation method for particle motion trajectories is improved. By introducing formulas for the probability of inter-particle collisions and the change in velocity after collision, as well as the rebound coefficient when particles collide with walls, the accuracy of discrete phase motion simulation is improved, and the motion trajectories of fly ash particles within the boiler are more realistically reflected.

[0084] Gas-Phase Combustion Model Optimization: The gas-phase combustion model combines a finite-rate chemical reaction model with a turbulent eddy dissipation model and introduces a reaction progress variable. During the simulation, the combustion reaction progress is tracked in real time, and the combustion model parameters are dynamically adjusted based on the reaction progress. For example, when the reaction progress reaches 50%, the reaction rate constant in the finite-rate chemical reaction model is increased by 10% to more accurately simulate the combustion process and improve the accuracy of the combustion simulation.

[0085] Optimization of the char oxidation model: The char oxidation process was simulated using an improved coal surface reaction mechanism, taking into account the impact of char microstructural changes on the oxidation reaction. Quantum chemical calculation methods were used to accurately calculate the activation energy and reaction rate constant of the char surface reaction. Quantum mechanics software calculated the activation energy of this type of char to be 80 kJ / mol and the reaction rate constant to be 0.05 s⁻¹. Based on these precise data, the char oxidation model was optimized to better reflect the actual char oxidation process.

[0086] Devolatilization Model Optimization: A chemical permeation devolatilization (CPD) model was used to simulate the devolatilization of coal, combined with experimental coal pyrolysis data. Pyrolysis experiments were conducted on this type of coal to obtain volatile analysis data at different temperatures and times. Machine learning algorithms (such as support vector machines) were used to optimize the CPD model parameters, adjusting the reaction rate and activation energy parameters within the model to more accurately simulate the coal devolatilization process.

[0087] Radiation Model Optimization: Utilizing deep learning algorithms, we study and analyze radiation heat transfer data within the furnace. We collect a large amount of radiation heat transfer intensity data from various locations within the furnace as a training set, and train a deep neural network model. This trained model accurately predicts the radiation heat transfer intensity at any location within the furnace, enabling the development of a more accurate radiation heat transfer model and improving the accuracy of radiation heat transfer simulations.

[0088] In a second aspect, an embodiment of the present invention further provides a device for constructing a mathematical model of a coal-fired boiler, comprising:

[0089] Acquisition Unit 21: This unit utilizes dedicated hardware and incorporates the operating program for the automated data acquisition system. The hardware is equipped with a high-speed data transmission interface, allowing simultaneous connection to scanners, design drawing storage devices, and the DCS system data interface. During operation, the unit utilizes an optical character recognition (OCR) technology module to rapidly identify scanned paper documents, converting key information such as the boiler's rated evaporation capacity and steam parameters into digital signals. A data interface program extracts geometric parameters from design software databases such as AutoCAD in real time. An image recognition module, based on a deep learning algorithm, continuously analyzes the DCS sensor point layout diagram to ensure accurate and efficient acquisition of design parameters.

[0090] The first construction unit 22: Built on a high-performance computing server, it integrates parametric modeling technology, intelligent assembly technology, multi-source data fusion technology, and parallel computing technology. In terms of parametric modeling, it is deeply integrated with SolidWorks software. Through a custom-developed interface, the design parameters obtained by the acquisition unit are seamlessly imported into the model parameter table, enabling the rapid generation and updating of 3D models. Intelligent assembly technology uses a pre-defined assembly rule library to intelligently match the connection relationships between boiler components and automatically complete component assembly. Multi-source data fusion technology uses specialized data processing algorithms to standardize and correlate historical operating data, real-time monitoring data, and theoretical calculation data to generate high-quality simulation data sets. Parallel computing technology utilizes the server's multi-core processor architecture to parallelize numerical simulation tasks under different operating conditions, accelerating the construction of physical models.

[0091] The second construction unit, Unit 23, is deployed on a specialized computing cluster and utilizes adaptive meshing technology and optimized models to establish a mathematical model of a coal-fired boiler. Working in conjunction with Ansys Fluent software, the adaptive meshing technology automatically adjusts the mesh density based on the changing characteristics of the flow and temperature fields within the boiler. For optimized turbulence models and discrete phase models, this unit accurately applies correction coefficients, improved calculation methods, and other parameters to the model calculations by invoking corresponding algorithm libraries. For example, in turbulence model calculations, a predetermined correction coefficient of 1.2 is automatically substituted, and in discrete phase model calculations, an improved particle trajectory calculation method is used to ensure that the mathematical model accurately simulates the complex physical and chemical processes within the boiler.

[0092] An embodiment of the present invention also provides a computer-readable storage medium. An industrial-grade solid-state drive (SSD) is selected as a computer-readable storage medium, and the coal-fired boiler mathematical model construction program of the present invention is stored therein. The program adopts C++ and Python mixed programming, C++ is used for efficient implementation of the core algorithm, and Python is used for data processing and model calling. When the operation and maintenance personnel of the coal-fired boiler need to build a new mathematical model, the storage medium is connected to the computer system, and the processor (such as Intel Xeon series) reads the program instructions therein. The program first calls the automatic data acquisition system module to execute the step of obtaining the design parameters of the coal-fired boiler; then runs the physical model construction related code, and uses the Solidworks API interface to implement parametric modeling and intelligent assembly operations; then executes the mathematical model construction program, calls Ansys Fluent and other software for meshing and model calculation, and finally completes the entire mathematical model construction process, realizing automatic model construction based on stored programs.

[0093] An embodiment of the present invention also provides an electronic device. A high-performance workstation is used as the electronic device, which is configured with an Intel Xeon W-2295 processor, 128GB of memory, and an NVIDIA Quadro RTX 6000 graphics card. The workstation is pre-installed with a Windows Server operating system and various software required by the present invention, including an automated data acquisition system, Solidworks, Ansys Fluent, etc. When the operation and maintenance personnel start the model building application on the electronic device, the processor automatically calls the program instructions in the memory. First, the design parameters of the coal-fired boiler are obtained from an external data source through the automated data acquisition system; then, according to the program logic, the physical model building and mathematical model building steps are executed in sequence. During the execution process, the parallel computing power of the graphics card is used to accelerate the numerical simulation calculation, quickly generate an accurate mathematical model of the coal-fired boiler, and help the operation and maintenance personnel efficiently complete the simulation and analysis of the boiler operation status.

[0094] The above-mentioned example, focusing on a 600MW supercritical coal-fired boiler, demonstrates that the method and apparatus for constructing a mathematical model for a coal-fired boiler, as provided by the present invention, employ innovative technologies at every stage, from acquiring design parameters to constructing both the physical and mathematical models. An automated data acquisition system ensures efficient and accurate acquisition of design parameters. During physical model construction, the application of parametric modeling, intelligent assembly, multi-source data fusion, and parallel computing technologies improves modeling efficiency and quality. During mathematical model construction, adaptive meshing technology and optimization of various basic models significantly enhance simulation accuracy.

[0095] The mathematical model construction device, computer-readable storage medium, and electronic device for coal-fired boilers provided in embodiments of the present invention, in close coordination with the construction method, can effectively address the current shortage of coal-fired boiler operation simulation methods. The mathematical model constructed based on the present invention can accurately simulate the operating state of coal-fired boilers, helping personnel to promptly identify and resolve problems such as local overheating and incomplete fuel combustion, thereby improving the safety and economic efficiency of coal-fired boiler operation. The invention possesses significant practicality and innovation, complies with the patent law's requirements for inventions, and possesses excellent value for widespread application.

[0096] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from all points of view, the embodiments should be regarded as illustrative and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and range of equivalents of the claims are included in the present invention. Any reference signs in the claims should not be construed as limiting the claim to which they relate.

[0097] In addition, it should be understood that although this specification describes the embodiments, not every embodiment contains only one independent technical solution. This description is for clarity only. Those skilled in the art should consider the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for the purpose of illustrating the technical concept of the present invention and cannot be used to limit the scope of protection of the present invention. Any changes made based on the technical solution in accordance with the technical concept proposed by the present invention fall within the scope of protection of the claims of the present invention.

Claims

1. A method for constructing a mathematical model of a coal-fired boiler, characterized in that: include: Obtain design parameters of coal-fired boilers; constructing a physical model of the coal-fired boiler based on the design parameters; A mathematical model of the coal-fired boiler is constructed based on the physical model.

2. The method for constructing a mathematical model of a coal-fired boiler according to claim 1, wherein: The design parameters of the coal-fired boiler are obtained by adopting an automated data acquisition system, using optical character recognition technology and data interface technology to extract data from the boiler finalized manual and design drawings, and extracting coordinate information from the DCS sensor measurement point layout position map through an image recognition algorithm.

3. The method for constructing a mathematical model of a coal-fired boiler according to claim 1, wherein: The constructing of the physical model of the coal-fired boiler based on the design parameters includes: performing three-dimensional modeling of the physical model of the coal-fired boiler based on modeling software, introducing parametric modeling technology into the modeling process, and using intelligent assembly technology to assemble components.

4. The method for constructing a mathematical model of a coal-fired boiler according to claim 3, characterized in that: The physical model of the coal-fired boiler constructed based on the design parameters further includes: obtaining a simulation data set based on the design parameters of the coal-fired boiler, and obtaining numerical simulation results of the coal-fired boiler mixture under different operating conditions, wherein the simulation data set is obtained using multi-source data fusion technology, and the numerical simulation results are obtained using parallel computing technology.

5. The method for constructing a mathematical model of a coal-fired boiler according to claim 1, wherein: The mathematical model of the coal-fired boiler is constructed based on the physical model, including: meshing the mathematical model of the coal-fired boiler, using adaptive meshing technology, and adjusting the mesh density according to the internal flow field and temperature field of the boiler.

6. The method for constructing a mathematical model of a coal-fired boiler according to claim 5, characterized in that: The mathematical model of the coal-fired boiler constructed based on the physical model also includes: establishing a mathematical model of the coal-fired boiler based on basic conservation equations, turbulence model, discrete phase model, gas phase combustion model, coal char oxidation model, devolatilization model and radiation model, and optimizing and improving each model.

7. The method for constructing a mathematical model of a coal-fired boiler according to claim 6, characterized in that: The turbulence model adopts the realizable k-ε model, and the influence of the complex flow boundary conditions in the boiler and the combustion process on the turbulence is considered by introducing a correction coefficient.

8. The method for constructing a mathematical model of a coal-fired boiler according to claim 6, wherein: The discrete phase model adopts a random trajectory model and improves the particle motion trajectory calculation method by considering the interaction between particles and the collision and rebound characteristics with the wall.

9. A device for constructing a mathematical model of a coal-fired boiler, characterized in that: include: An acquisition unit, used for acquiring design parameters of a coal-fired boiler; A first construction unit is configured to construct a physical model of the coal-fired boiler based on the design parameters; The second construction unit is configured to construct a mathematical model of the coal-fired boiler based on the physical model.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, and when the program is executed by a processor, the steps of the method for constructing a mathematical model of a coal-fired boiler according to any one of claims 1 to 8 are implemented.

Images