Design optimization method and system for waste heat utilization heating surface of liquid deslagging boiler

Abstract

The invention discloses a liquid slagging boiler waste heat utilization heating surface design optimization method and system, and relates to the technical field of liquid slagging boilers, and the method comprises the following steps: carrying out the thermal analysis of an initial finite element mesh model of the waste heat utilization heating surface of the liquid slagging boiler, generating the heat flux density distribution of the waste heat utilization heating surface, and calculating the heat flux density distribution of the waste heat utilization heating surface; acquiring corresponding heat exchange efficiency based on the heat flow density distribution; constructing an optimization problem by taking material distribution of the waste heat utilization heating surface as an optimization variable and taking heat exchange efficiency maximization and material consumption minimization as objective functions; the optimization problem is solved, in the solving process, the waste heat utilization heating surface is divided into a plurality of design units, and the relative density of each design unit changes between 0 and 1; and gradually adjusting the relative density of each unit until the optimization objective function reaches the optimal value. Topological optimization is combined with the design process of the waste heat utilization heating surface of the liquid slagging boiler, so that the optimized layout of the heating surface structure is realized, and the heat exchange efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of liquid slag discharge boiler technology, and in particular to a method and system for optimizing the design of the heat transfer surface for waste heat utilization in liquid slag discharge boilers. Background Technology

[0002] Liquid slag discharge boilers play an important role in industrial production. If the large amount of waste heat generated during their operation can be effectively recovered and utilized, energy efficiency can be significantly improved.

[0003] A liquid slag discharge boiler is a special type of boiler that discharges ash and slag by melting them into a liquid state. The waste heat recovery surface is a key component for recovering the physical heat of the high-temperature flue gas and liquid slag, primarily functioning to improve boiler thermal efficiency and reduce flue gas temperature and slag physical heat loss. The liquid slag temperature in a liquid slag discharge boiler can reach 1300~1500℃, and the flue gas temperature is approximately 150~250℃. The waste heat recovery surface recovers heat through radiation, convection, and heat conduction. Radiative heat transfer involves the furnace water-cooled walls directly absorbing the radiant heat from the flame and liquid slag; convection heat transfer occurs through the superheater, reheater, economizer, and air preheater transferring heat via flue gas flow; and heat conduction occurs through the metal walls of the heat recovery surface transferring heat to the working fluid (water, steam, or air).

[0004] However, the design of waste heat recovery surfaces in traditional liquid slag discharge boilers is mostly based on experience and conventional engineering calculation methods, which have many limitations. On the one hand, the structural layout of the heating surfaces often lacks systematic optimization, resulting in uneven heat exchange, which not only affects heat exchange efficiency but also reduces the service life of the heating surfaces. On the other hand, it is difficult to achieve optimal material utilization while meeting specific heat exchange requirements, resulting in resource waste and increased costs. Summary of the Invention

[0005] Based on the deficiencies of the existing technology, the present invention provides a method and system for optimizing the design of the heating surface for waste heat utilization in liquid slag discharge boilers, which solves the existing problems.

[0006] The present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing the design of a heating surface for waste heat utilization in a liquid slag discharge boiler, comprising the following steps: Thermal analysis is performed on the initial finite element mesh model of the waste heat utilization heating surface of the liquid slag discharge boiler to generate the heat flux density distribution of the waste heat utilization heating surface, and the corresponding heat exchange efficiency is obtained based on the heat flux density distribution. An optimization problem is constructed by taking the material distribution of the waste heat utilization heating surface as the optimization variable and the maximization of heat exchange efficiency and the minimization of material usage as the objective functions. The optimization problem is solved, and the material distribution of the waste heat utilization heating surface is optimized based on the solution process. During the solution process, the waste heat utilization heating surface is divided into multiple design units, and the relative density of each design unit varies between 0 and 1. A relative density of 0 indicates that there is no material in the unit, and a relative density of 1 indicates that the unit is completely made of material. The relative density of each unit is gradually adjusted until the optimization objective function reaches the optimal value.

[0007] Preferably, the objective function is as follows: ; In the formula, F Let be the objective function. η For heat exchange efficiency, V For material volume, V 0 Based on the reference volume, and These are the weighting coefficients.

[0008] Preferably, in the process of solving the optimization problem, the objective function needs to reach the optimal value under the premise of satisfying the constraints, wherein the constraints include physical and thermodynamic constraints, structural and mechanical constraints, and safe operation constraints.

[0009] Preferably, the construction of the initial finite element mesh model includes: An initial geometric model of the heat transfer surface for waste heat utilization in a liquid slag discharge boiler is generated based on geometric shape and size parameters. The initial geometric model is meshed to obtain the initial finite element mesh model.

[0010] Preferred options also include: The optimized material distribution is then converted into geometric and dimensional parameters. Reconstruct the geometric model based on its shape and size parameters to generate a new geometric model; Thermal analysis was performed on the new geometric model to calculate its new performance and economic indicators. Obtain the initial performance and economic indicators of the initial geometric model, and compare the new performance and economic indicators with the initial performance and economic indicators; if the preset improvement requirements are met, output the new geometric model; otherwise, perform optimization and solution again.

[0011] Secondly, the present invention provides a system for optimizing the design of a heat transfer surface for waste heat utilization in a liquid slag discharge boiler, comprising: The analysis module is used to perform thermal analysis on the initial finite element mesh model of the waste heat utilization heating surface of the liquid slag discharge boiler, generate the heat flux density distribution of the waste heat utilization heating surface, and obtain the corresponding heat exchange efficiency based on the heat flux density distribution. The module is used to construct an optimization problem with the material distribution of the waste heat utilization heating surface as the optimization variable and the objective functions of maximizing heat exchange efficiency and minimizing material usage. The optimization module is used to solve the optimization problem and optimize the material distribution of the waste heat utilization heating surface based on the solution process. During the solution process, the waste heat utilization heating surface is divided into multiple design units, and the relative density of each design unit varies between 0 and 1. A relative density of 0 indicates that there is no material in the unit, and a relative density of 1 indicates that the unit is completely made of material. The relative density of each unit is gradually adjusted until the optimization objective function reaches the optimal value.

[0012] Compared with the prior art, the above-mentioned at least one technical solution adopted by the present invention can achieve the following beneficial effects: This invention first uses the material distribution of the waste heat utilization heating surface as the optimization variable, and sets the objective functions as maximizing heat exchange efficiency and minimizing material consumption to construct an optimization problem. Through mathematical algorithms, the material layout is redistributed to maximize structural performance and optimize resource efficiency. The optimization problem is solved by optimizing the material distribution of the waste heat utilization heating surface. During the solution process, the waste heat utilization heating surface is divided into multiple design units, with the relative density of each unit varying between 0 and 1. A relative density of 0 indicates no material in the unit, while a relative density of 1 indicates that the unit is entirely made of material. The relative density of each unit is gradually adjusted until the optimization objective function reaches its optimal value. By combining topology optimization with the design process of the waste heat utilization heating surface of a liquid slag discharge boiler, the optimized layout of the heating surface structure is achieved, improving heat exchange efficiency, and reducing unnecessary material redundancy by optimizing the material distribution. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart illustrating the design optimization method for the heating surface of a liquid slag discharge boiler for waste heat utilization according to the present invention. Figure 2 This is a flowchart of a method for optimizing the design of a heat transfer surface for utilizing waste heat in a liquid slag discharge boiler, according to the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Figure 1 A flowchart illustrating an optimized design method for the heat transfer surface of a liquid slag discharge boiler for waste heat utilization, provided in an embodiment of the present invention. Figure 2 This document provides a complete illustrated method for optimizing the design of a heat transfer surface for waste heat utilization in a liquid slag discharge boiler, as provided in an embodiment of the present invention. The following is a detailed explanation in conjunction with... Figure 1 and Figure 2 This invention provides a detailed description of an optimization method for the design of a heat transfer surface for waste heat utilization in a liquid ash discharge boiler. Specifically, it relates to a topology optimization-based method for optimizing the design of the heat transfer surface in a liquid ash discharge boiler. By combining topology optimization with the design process of the heat transfer surface, the optimized layout of the heat transfer surface structure is achieved, improving heat exchange efficiency and reducing costs, thereby enhancing the overall performance and economy of the liquid ash discharge boiler waste heat utilization system. The method specifically includes the following steps:

[0017] S1: Mesh the initial geometric model of the waste heat utilization heating surface of the liquid slag discharge boiler to obtain a finite element mesh model.

[0018] An initial geometric model of the waste heat recovery surface of a liquid slag discharge boiler was constructed using computer-aided design (CAD) software. This model includes information such as the basic shape and dimensions of the heating surface, as well as its connections to other boiler components. Simultaneously, the model was meshed to generate a finite element mesh model, providing a foundation for subsequent thermal analysis and topology optimization. Adaptive meshing technology was employed, automatically refining the mesh in regions with large temperature gradients to ensure computational accuracy, while appropriately sparsening the mesh in regions with gentle temperature changes to reduce computational load.

[0019] When constructing the initial geometric model of the heat transfer surface for waste heat recovery in a liquid slag boiler, professional CAD software such as SolidWorks and ANSYS DesignModeler is used. The basic shape of the heat transfer surface is designed based on the overall boiler structure and waste heat recovery process requirements. Dimensional parameters are determined based on factors such as boiler capacity, flue gas flow rate, and slag heat, ensuring the heat transfer surface can withstand the corresponding heat load. For mesh generation, adaptive meshing technology intelligently divides the mesh according to the geometric characteristics and thermophysical properties of the heat transfer surface. For example, for shell-and-tube heat transfer surfaces, in areas with large temperature gradients, such as the connection between the tube and shell, and the area inside the tube near the high-temperature flue gas or slag side, the mesh size can be set to 1mm~5mm to accurately capture temperature changes. In areas far from the connection points and heat inlets, the mesh size can be increased to 10mm~20mm. Tetrahedral or hexahedral elements can be used for the mesh. Tetrahedral elements are suitable for modeling complex geometries, while hexahedral elements offer advantages in computational accuracy and efficiency; the choice can be made based on specific needs.

[0020] S2: Perform thermal analysis on the finite element mesh model to obtain the heat flux density distribution of the heated surface under different operating conditions, and then calculate the heat transfer efficiency of the heated surface.

[0021] Thermal analysis was performed on a finite element mesh model of the heating surface based on the finite element analysis method. Operating parameters of the liquid slag discharge boiler and material properties of the heating surface were set. Boiler operating parameters included furnace temperature, flue gas flow rate, flue gas composition, liquid slag temperature and flow rate, etc., while heating surface material properties included thermal conductivity, specific heat capacity, and density. By solving the heat transfer equations, thermophysical quantities such as heat flux density distribution of the heating surface under different operating conditions were obtained.

[0022] The finite element method software used for thermal analysis can be ANSYS or ABAQUS, among others. When setting the boiler operating parameters, the furnace temperature can be selected between 1200°C and 1800°C depending on the fuel type and combustion conditions. The flue gas flow rate is determined within the range of 10 m³ / s to 100 m³ / s based on the boiler capacity. The liquid slag temperature is typically between 1300°C and 1600°C, and the flow rate is set according to the boiler slag discharge rate.

[0023] The formula for calculating heat exchange efficiency is: ; in, Q absorbed The heat absorbed by the heated surface is obtained by multiplying the heat flux density by the area of ​​the heated surface. Q total The total heat released by a heat source (such as flue gas) in this area can be calculated using the enthalpy difference between the inlet and outlet of the heat source: ; in, m The mass flow rate of the heat source is (kg / s). h in , h out The specific enthalpy is the import / export ratio (J / kg).

[0024] S3: Using the material distribution of the heated surface as the optimization variable, and taking the maximization of heat exchange efficiency and the minimization of material usage as the optimization objectives, the structure of the heated surface is optimized in combination with the constraints.

[0025] The optimization objective balances performance improvement and cost control, aiming to maximize heat transfer efficiency while minimizing material usage. Constraints are the boundary limitations of the optimization design, ensuring structural feasibility and safety. These constraints include physical and thermodynamic constraints (energy conservation and heat transfer equations), structural and mechanical constraints (stress, deformation, and stability), and safe operation constraints (pressure and temperature boundaries). Improving heat transfer efficiency typically increases material usage, requiring a balance through multi-objective optimization (such as weighted summation). The objective function can be expressed as:

[0026] ; in, η For heat exchange efficiency, V For material volume, V 0 Based on the reference volume, , are weighting coefficients, and ( ).

[0027] Topology optimization is employed for the structural optimization design of the heating surface. The material distribution of the heating surface is used as the optimization variable, with the optimization objectives being maximizing heat transfer efficiency and minimizing material usage. Topology optimization, based on mathematical methods such as the variable density method or the level set method, divides the heating surface into multiple design units. Through iterative calculations, the material density or interface position of each design unit is gradually adjusted to achieve the optimal value of the objective function while satisfying the constraints.

[0028] The variable density method uses the relative density of the design element as a design variable, where relative density represents the degree of material filling at a given location. An optimization algorithm is used to vary the relative density between 0 (no material) and 1 (complete material), thereby determining the optimal material distribution for the heated surface. The variable density method includes defining a control variable field. θ c The range is 0 to 1. (This is a definition / specification.) θ c =1 corresponds to fluid, while θ c =0 indicates a reverse osmosis coefficient. αFor permeable materials, the damping term is added to the Navier-Stokes equation:

[0029] ; In the formula, For fluid density, Let v be the dynamic viscosity, and v be the velocity vector in the x, y, and z directions. p For fluid pressure, This is the drag coefficient.

[0030] The Navier-Stokes equations describe the conservation of momentum in fluid flow and heat transfer. Adding a damping term to these equations... θ c It can control the damping degree of the flow channel, thereby regulating the flow and heat transfer characteristics of the heated surface, and further optimize the optimization objective by combining the constraints.

[0031] In the fluid domain, the damping term is 0. These different values ​​approximate the no-slip boundary conditions at the interface between different domains.

[0032] The variable density method in topology optimization divides the heated surface into numerous tiny design elements by introducing a relative density variable, such as discretizing the heated surface into 10,000 to 100,000 elements. During the optimization process, the relative density of each element varies between 0 and 1, where a relative density of 0 indicates that the element contains no material, and a relative density of 1 indicates that the element contains all the material. Optimization algorithms employ methods such as optimization criteria or mathematical programming. Optimization criteria methods perform iterative calculations based on optimality conditions, while mathematical programming methods transform the optimization problem into a mathematical programming problem to be solved.

[0033] S4: The optimization results are used to reconstruct the geometric model of the heated surface, generating an optimized geometric model of the heated surface.

[0034] Based on the optimization results obtained in step S3, the geometric model of the heated surface is reconstructed. The optimized material distribution information is converted into specific geometric shape and size parameters to generate a new geometric model of the heated surface. Parametric modeling technology is used during the model reconstruction process to facilitate rapid modification and adjustment of the model.

[0035] In the optimization of heated surface structures, the process of transforming material distribution into a geometric model is essentially achieved through the mapping relationship between material distribution parameters and geometric features. The material distribution in the optimization result is usually represented by density values ​​(such as continuous values ​​between 0 and 1) or material type parameters (such as the proportion of different materials), while the geometric model requires specific parameters such as shape, size, and boundary coordinates. The conversion between the two requires establishing mapping rules. By setting a density threshold, regions above the threshold are defined as "retained material," and regions below the threshold are defined as "empty." Boolean operations are then used to generate the solid geometry.

[0036] Parametric modeling technology employs a feature-based parametric design method, using geometric features of the heated surface, such as length, diameter, and thickness, as parameter variables. During model reconstruction, the values ​​of these parameter variables are determined based on the material distribution information obtained from topology optimization. For example, for regions with denser material distribution after optimization, the thickness parameter is appropriately increased; for regions with sparse material distribution, the thickness parameter is reduced while meeting strength and heat transfer requirements. Rationality checks include verifying the model's geometric connectivity and non-interference properties using geometric analysis algorithms. Optimization involves smoothing the model, removing sharp corners and small geometric defects using surface fitting algorithms or rounded corner transition algorithms. Simplification, based on manufacturing process requirements, removes small features with minimal impact on heat transfer and structural strength, such as tiny holes and slots, using feature suppression or model simplification software tools to reduce manufacturing costs and processing difficulty.

[0037] S5: Evaluate the performance of the reconstructed heat transfer surface model, including thermal analysis to calculate its heat exchange efficiency and other performance indicators, and comprehensively consider economic indicators such as manufacturing cost and operation and maintenance cost, and compare the performance with the initial heat transfer surface model to determine the optimization effect.

[0038] The reconstructed heat transfer surface model is then evaluated for performance. Thermal analysis is performed again on the reconstructed model to calculate its heat transfer efficiency and other performance indicators, which are then compared with the performance of the initial heat transfer surface model. The performance evaluation employs a multi-indicator comprehensive evaluation method, such as the Analytic Hierarchy Process (AHP), assigning appropriate weights to different performance indicators and obtaining a comprehensive evaluation score through weighted summation, thereby comprehensively and objectively evaluating the optimization effect of the heat transfer surface.

[0039] When calculating heat transfer efficiency through thermal analysis, the ratio of actual heat transfer power to theoretical maximum heat transfer power is calculated using the law of conservation of energy, based on parameters such as the temperature difference and flow rate of the medium at the inlet and outlet of the heated surface. Manufacturing cost calculation considers material costs, processing costs, and assembly costs. Material costs are calculated based on the volume of the heated surface and the unit price of the material. Processing costs are estimated based on the complexity of the processing technology and processing time. Assembly costs are determined by considering the number of components and the difficulty of assembly.

[0040] Based on the same concept, the present invention also provides a design optimization system for the heat transfer surface of a liquid slag discharge boiler for waste heat utilization, including a division module, an analysis module, a construction module, an optimization module, a reconstruction module and an evaluation module.

[0041] The meshing module is used to mesh the initial geometric model of the waste heat utilization heating surface of the liquid slag discharge boiler, resulting in a finite element mesh model.

[0042] The analysis module is used to perform thermal analysis on the initial finite element mesh model of the waste heat utilization heating surface of the liquid slag discharge boiler, generate the heat flux density distribution of the waste heat utilization heating surface, and obtain the corresponding heat exchange efficiency based on the heat flux density distribution.

[0043] The building module is used to construct an optimization problem with the material distribution of the waste heat utilization heating surface as the optimization variable and the maximization of heat exchange efficiency and the minimization of material usage as the objective functions.

[0044] The optimization module is used to solve the optimization problem and optimize the material distribution of the waste heat utilization heating surface based on the solution process. During the solution process, the waste heat utilization heating surface is divided into multiple design units. The relative density of each design unit varies between 0 and 1. A relative density of 0 indicates that there is no material in the unit, and a relative density of 1 indicates that the unit is made of material. The relative density of each unit is gradually adjusted until the optimization objective function reaches the optimal value.

[0045] The reconstruction module is used to reconstruct the geometric model of the heated surface based on the optimization results, and generate an optimized geometric model of the heated surface.

[0046] The evaluation module is used to evaluate the performance of the reconstructed heated surface model. If the preset performance improvement requirements are met, the optimized heated surface model design scheme is output; otherwise, it is returned to the optimization module for re-optimization.

[0047] Working principle of the invention: After the optimization system for the waste heat recovery surface of the liquid slag discharge boiler is started, the partitioning module first constructs an initial geometric model of the heating surface using professional CAD software based on the specific design requirements and operating parameters of the boiler. During the construction process, factors such as the overall structural layout of the boiler and the waste heat recovery process path are fully considered to determine the basic shape and approximate size range of the heating surface. Subsequently, adaptive meshing technology is used to mesh the initial geometric model. In key areas with large temperature gradients, such as near the flue gas inlet and the liquid-slag impact area, a dense mesh is generated to accurately capture the details of heat transfer and stress changes; while in areas with relatively gentle temperature changes, a sparse mesh is appropriately generated. This ensures computational accuracy while effectively controlling the computational scale and resource consumption, forming a finite element mesh model that can be used for subsequent analysis and calculation, and then transferring it to the analysis module.

[0048] The reconstruction module reconstructs the geometric model of the heated surface based on the optimization results obtained from the optimization module. It transforms the optimized material distribution information into specific geometric shapes and dimensional parameters, generating a new geometric model of the heated surface. Parametric modeling techniques are employed during the model reconstruction process to facilitate rapid modification and adjustment of the model.

[0049] The evaluation module performs a performance assessment on the reconstructed heat transfer surface model. The analysis module then performs a thermal analysis on the reconstructed model, calculating its heat transfer efficiency and other performance indicators, and comparing the results with the initial heat transfer surface model. The performance evaluation employs a multi-indicator comprehensive evaluation method, such as the Analytic Hierarchy Process (AHP), assigning appropriate weights to different performance indicators and obtaining a comprehensive evaluation score through weighted summation, thereby comprehensively and objectively evaluating the optimization effect of the heat transfer surface.

[0050] Regarding system connectivity and workflow, firstly, the partitioning module constructs an initial geometric model using CAD software based on the design requirements of the waste heat utilization heating surface of the liquid slag discharge boiler, performs mesh generation, generates a finite element mesh model, and then transmits the model to the analysis module.

[0051] After receiving the finite element mesh model, the analysis module performs thermal analysis calculations based on the set boiler operating parameters and the material properties of the heating surface to obtain thermophysical quantities such as the temperature field and heat flux density distribution of the heating surface, and then transmits these results to the optimization module.

[0052] Based on the thermophysical quantities provided by the analysis module, the optimization module, combined with the set optimization objectives and constraints, uses topology optimization to optimize the material distribution of the heated surface, obtains the optimized material distribution information, and transmits it to the reconstruction module.

[0053] Based on the optimization results from the optimization module, the reconstruction module uses parametric modeling technology to reconstruct the geometric model of the heated surface, generating a new geometric model of the heated surface, and then transmits the model to the evaluation module.

[0054] The evaluation module performs thermal analysis and comprehensive performance evaluation on the reconstructed heat transfer surface model, calculating its heat exchange efficiency, manufacturing cost, and other indicators, and comparing it with the initial model. If the optimized model meets the preset performance improvement requirements, it outputs the optimized heat transfer surface model design scheme; otherwise, it returns to the optimization module to adjust the optimization objectives or constraints, and recalculates until a satisfactory optimization result is obtained.

[0055] After receiving the finite element mesh model from the partitioning module, the analysis module performs detailed settings based on the actual operating parameters of the liquid slag discharge boiler. These operating parameters include information such as furnace temperature, flue gas flow rate, flue gas composition, and liquid slag temperature and flow rate, while also incorporating the material properties of the heating surface itself, including physical characteristics such as thermal conductivity, specific heat capacity, and density.

[0056] The optimization module takes the thermophysical data provided by the analysis module as its core input and the material distribution of the heated surface as the main optimization variable. It conducts in-depth structural optimization design work closely around the three key optimization objectives of maximizing heat transfer efficiency and minimizing material usage. For example, based on the variable density method, the heated surface is meticulously divided into numerous tiny design units, and each unit is assigned a relative density variable that can flexibly vary between 0 (representing no material) and 1 (representing all material).

[0057] Based on the optimization results output by the optimization module, the reconstruction module comprehensively reconstructs the geometric model of the heated surface using parametric modeling technology. The optimized material distribution information is transformed into specific geometric shapes and dimensional parameters to generate the geometric model of the heated surface. During the reconstruction process, the model undergoes rigorous rationality checks and optimizations according to manufacturing process requirements. Geometric connectivity and non-interference are carefully checked using geometric analysis algorithms to ensure that the model is free of structural defects and conflicts during actual manufacturing and assembly. Surface fitting algorithms or fillet transition algorithms are used to smooth the model, effectively removing sharp corners and minor geometric defects, improving the model's manufacturing feasibility and structural stability. Simultaneously, according to the actual needs of the manufacturing process, feature suppression or specialized model simplification software tools are used to remove minor features with minimal impact on heat transfer efficiency and structural strength, such as tiny holes and slots. This significantly reduces manufacturing costs and processing difficulty without affecting overall performance. The reconstructed model is then transferred to the evaluation module.

[0058] The evaluation module conducts a comprehensive and in-depth performance evaluation of the reconstructed heat transfer surface model. First, the analysis module performs detailed thermal analysis calculations on the reconstructed model again to accurately obtain key performance indicators such as heat transfer efficiency. When calculating heat transfer efficiency, based on parameters such as the temperature difference and flow rate of the inlet and outlet media of the heat transfer surface, the ratio of actual heat transfer power to the theoretical maximum heat transfer power is calculated strictly according to the law of conservation of energy, thereby accurately evaluating its heat transfer performance. In addition, important economic indicators such as the manufacturing cost and operation and maintenance cost of the heat transfer surface are comprehensively considered. If the comprehensive evaluation score of the optimized model meets the preset performance improvement requirements, the optimization is considered successful, and the system will output the optimized heat transfer surface model design scheme; if not, the system automatically returns to the optimization module, readjusts the optimization objectives or constraints, and restarts the optimization calculation process until a satisfactory optimization result is obtained.

[0059] Regarding system installation and debugging, check the compatibility between the software and computer hardware (such as graphics cards, memory, processors, etc.) to ensure that the software can run stably and fully utilize its graphics processing and model building functions. For mesh generation tools, perform detailed settings according to their installation guide, including specifying mesh generation algorithm parameters and setting mesh quality check standards, to ensure that the generated finite element mesh model meets the requirements of subsequent thermal analysis and topology optimization.

[0060] The finite element analysis software (such as ANSYS, ABAQUS, etc.) upon which the analysis module relies is installed on a server or workstation. After installation, the software system environment needs to be configured, such as setting solver parameters and defining material library paths. Simultaneously, the data transmission interface between the software and the meshing module needs to be tested and debugged to ensure that the finite element mesh model can be accurately transferred from the meshing module to the analysis module, and that the model data can be correctly identified and read in the analysis module. When setting boiler operating parameters and heating surface material properties, the accuracy and completeness of parameter input should be ensured based on the design documents and operation manual of the actual liquid slag discharge boiler. The operating parameters can be fine-tuned and calibrated by comparing them with actual boiler operating data to improve the reliability of the thermal analysis results.

[0061] As the core optimization engine of the system, the optimization module's related algorithm libraries and calculation programs must be installed on a dedicated optimization computing server. During installation, the corresponding mathematical libraries and computing resources must be configured according to the requirements of the selected topology optimization (such as the variable density method or the level set method). For example, for the variable density method optimization algorithm, it is necessary to ensure that the server has sufficient memory to store a large amount of relative density data of design cells, and to configure an efficient linear algebra computing library to accelerate the iterative calculation process. When debugging the optimization module, simple test cases can be used to verify the algorithm, check whether the optimization algorithm can correctly calculate according to the preset optimization objectives and constraints, and gradually adjust the optimization parameters (such as the number of iterations, convergence criteria, etc.) to achieve the best optimization effect.

[0062] The parametric modeling software used in the reconstruction module must be seamlessly integrated with the optimization and evaluation modules. During installation, it is essential to ensure that the reconstruction module can accurately read the optimization results data output by the optimization module and successfully transfer the reconstructed model data to the evaluation module. During model reconstruction, the model generation algorithm in the parametric modeling software must be tested and optimized to ensure that the reconstructed heated surface geometry meets design requirements in terms of shape, size, and structural integrity. Multiple model examples with different optimization results can be generated and compared with the original design model to check the accuracy and rationality of the model reconstruction.

[0063] During the installation of the evaluation module, it is necessary to integrate thermal analysis software, cost estimation tools, and multi-index comprehensive evaluation software. Smooth data exchange between these software programs is crucial; for example, the heat exchange efficiency data output by the thermal analysis software must be accurately read and used in the comprehensive evaluation calculations. When debugging the evaluation module, the calculation methods and weight settings for each performance index must be thoroughly verified. By comparing with actual heat transfer surface performance data or empirical data, the calculation methods and weight values ​​should be adjusted to ensure that the comprehensive evaluation results accurately reflect the optimization effect of the heat transfer surface. During the overall system debugging phase, starting from the module division, test data should be input sequentially to check the data transfer between modules, the output of calculation results, and the normal operation of the overall optimization process. Through repeated debugging with multiple test cases under different operating conditions and design requirements, the system can be ensured to operate stably and efficiently, and to output accurate and reliable heat transfer surface optimization design schemes.

[0064] II. Evidence of the Relevant Effects of the Embodiments. The embodiments of the present invention have achieved some positive effects during research and development or use, and indeed possess significant advantages compared to existing technologies. The following description, combined with data and charts from the experimental process, illustrates these advantages.

[0065] Assume a liquid slag discharge boiler with a design capacity of 50 t / h, a furnace temperature of approximately 1500°C during stable operation, a flue gas flow rate of 30 m³ / s, and flue gas components mainly including carbon dioxide, water vapor, nitrogen, and small amounts of sulfur dioxide. The liquid slag temperature is 1400°C, and the flow rate is 2 t / h. First, the meshing module uses SolidWorks software to construct the initial geometric model of the heating surface based on these parameters and the overall structural design requirements of the boiler. The heating surface is designed as a shell-and-tube structure, with stainless steel tubes and carbon steel shell. During model construction, the tube diameter is determined to be 50 mm, the length 3 m, and the shell thickness 10 mm. The length and width are determined based on the boiler layout. Adaptive meshing technology is used for mesh generation. At the tube-shell connection points and in the tube region on the flue gas inlet side, the mesh size is set to 2 mm, and hexahedral elements are used for mesh generation, resulting in approximately 50,000 elements. The generated finite element mesh model is then transferred to the analysis module.

[0066] After receiving the finite element mesh model in ANSYS software, the analysis module sets the boiler operating parameters and the material properties of the heating surfaces. Stainless steel has a thermal conductivity of 18 W / (m·K), a specific heat capacity of 500 J / (kg·K), and a density of 7950 kg / m³; carbon steel has a thermal conductivity of 50 W / (m·K), a specific heat capacity of 480 J / (kg·K), and a density of 7850 kg / m³. The maximum temperature of the heating surfaces reaches 800°C. The heat flux density is relatively high in the flue gas inlet region, approximately 1000 W / m², gradually decreasing to 200 W / m² in the outlet region. The thermal analysis results are then transferred to the optimization module.

[0067] The optimization module employs a variable density method, dividing the heated surface into 80,000 design units. With the optimization objectives of maximizing heat transfer efficiency and minimizing material usage, iterative calculations are performed using optimization criteria. After 100 iterations, the optimization algorithm converges. The optimization results show that in regions with high heat flux density, the relative density of the tubes increases, resulting in a more concentrated material distribution; conversely, in regions with low heat flux density, the relative density decreases, even approaching zero in some areas. The optimized material distribution information is then transmitted to the reconstruction module.

[0068] The reconstruction module reconstructs the model in CATIA software based on the optimization results. It adjusts the pipe diameter and thickness parameters according to changes in relative density. In areas with concentrated material distribution, the pipe diameter is increased to 55mm and the thickness to 5.5mm; in areas with sparse material distribution, the pipe diameter is reduced to 45mm and the thickness to 4mm. The reconstructed model undergoes a rationality check to ensure there are no geometric interferences or connectivity issues. Then, it is smoothed and simplified, removing minor chamfers and hole features. The reconstructed model is then transferred to the evaluation module.

[0069] The evaluation module again used ANSYS software to perform thermal analysis on the reconstructed model. Regarding manufacturing costs, by calculating material costs, processing costs, and assembly costs, it was found that material costs were reduced by approximately 10% due to the more reasonable material distribution after optimization, processing costs were slightly reduced due to model simplification, and assembly costs remained essentially unchanged. Taking into account operating and maintenance costs, the analytic hierarchy process (AHP) was used, assigning a weight of 0.5 to heat exchange efficiency and 0.2 to cost. The system outputs an optimized design scheme for the heating surface model. This scheme can be applied to the manufacturing and modification of actual liquid slag discharge boilers, effectively improving the boiler's waste heat utilization efficiency, reducing costs, and extending the service life of the heating surface.

[0070] Optimizing the heat transfer surface of a liquid ash discharge boiler by topology optimization allows for adjustments to material distribution based on heat flux distribution. In areas of high heat flux density, increasing material distribution or optimizing the structure, such as in key heat exchange areas like the flue gas inlet, makes the heat transfer surface structure more aligned with the heat transfer path, effectively enhancing the heat exchange process and significantly improving heat exchange efficiency.

[0071] Topology optimization design enables precise determination of the required material quantities for each part of the heating surface while meeting requirements for heat transfer performance and structural strength, avoiding excessive material usage. Optimizing material distribution reduces unnecessary material redundancy. Overall, this significantly reduces the total lifecycle cost of the liquid slag discharge boiler waste heat recovery system, improving the company's economic efficiency and market competitiveness.

[0072] Optimizing the heat exchange surface improves heat transfer efficiency and reduces costs, significantly enhancing the overall waste heat utilization performance of the liquid ash discharge boiler. Under different operating conditions, such as changes in boiler load and fuel type adjustments, the optimized heat exchange surface can better adapt to heat load fluctuations and maintain stable and efficient heat exchange performance.

[0073] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0074] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for optimizing the design of a heat transfer surface for waste heat utilization in a liquid slag discharge boiler, characterized in that, Includes the following steps: Thermal analysis is performed on the initial finite element mesh model of the waste heat utilization heating surface of the liquid slag discharge boiler to generate the heat flux density distribution of the waste heat utilization heating surface, and the corresponding heat exchange efficiency is obtained based on the heat flux density distribution. An optimization problem is constructed by taking the material distribution of the waste heat utilization heating surface as the optimization variable and the maximization of heat exchange efficiency and the minimization of material usage as the objective functions. The optimization problem is solved, and the material distribution of the waste heat utilization heating surface is optimized based on the solution process. During the solution process, the waste heat utilization heating surface is divided into multiple design units. The relative density of each design unit varies between 0 and 1. A relative density of 0 indicates that there is no material in the unit, and a relative density of 1 indicates that the unit is made of material. The relative density of each unit is gradually adjusted until the objective function reaches the optimal value.

2. The method for optimizing the design of the heating surface for waste heat utilization in a liquid slag discharge boiler as described in claim 1, characterized in that, The specific objective function is as follows: ； In the formula, F Let be the objective function. η For heat exchange efficiency, V For material volume, V 0 Based on the reference volume, and These are the weighting coefficients.

3. The method for optimizing the design of the heating surface for waste heat utilization in a liquid slag discharge boiler as described in claim 1, characterized in that, In the process of solving the optimization problem, the objective function needs to be optimized to the optimal value under the premise of satisfying the constraints. The constraints include physical and thermodynamic constraints, structural and mechanical constraints, and safe operation constraints.

4. The method for optimizing the design of the heating surface for waste heat utilization in a liquid slag discharge boiler as described in claim 1, characterized in that, The construction of the initial finite element mesh model includes: An initial geometric model of the heat transfer surface for waste heat utilization in a liquid slag discharge boiler is generated based on geometric shape and size parameters. The initial geometric model is meshed to obtain the initial finite element mesh model.

5. The method for optimizing the design of the heating surface for waste heat utilization in a liquid slag discharge boiler as described in claim 4, characterized in that, Also includes: The optimized material distribution is then converted into geometric and dimensional parameters. Reconstruct the geometric model based on its shape and size parameters to generate a new geometric model; Thermal analysis was performed on the new geometric model to calculate its new performance and economic indicators. Obtain the initial performance and economic indicators of the initial geometric model, and compare the new performance and economic indicators with the initial performance and economic indicators; if the preset improvement requirements are met, output the new geometric model; otherwise, perform optimization and solution again.

6. A system for optimizing the design of a heat transfer surface for waste heat utilization in a liquid slag discharge boiler, characterized in that, include: The analysis module is used to perform thermal analysis on the initial finite element mesh model of the waste heat utilization heating surface of the liquid slag discharge boiler, generate the heat flux density distribution of the waste heat utilization heating surface, and obtain the corresponding heat exchange efficiency based on the heat flux density distribution. The module is used to construct an optimization problem with the material distribution of the waste heat utilization heating surface as the optimization variable and the objective functions of maximizing heat exchange efficiency and minimizing material usage. The optimization module is used to solve optimization problems and optimize the material distribution of the waste heat utilization heating surface based on the solution process. During the solution process, the waste heat utilization heating surface is divided into multiple design units. The relative density of each design unit varies between 0 and 1. A relative density of 0 indicates that there is no material in the unit, and a relative density of 1 indicates that the unit is made of material. The relative density of each unit is gradually adjusted until the objective function reaches the optimal value.

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