A blast furnace bell-less top distribution integration simulation method and system

Abstract

The application discloses a kind of blast furnace bell-less top distribution integration simulation method and system, belong to blast furnace ironmaking bell-less top distribution simulation and optimization technical field.System uses five logical architectures of data source and interface layer, core function management layer, core simulation and calculation layer, result post-processing and analysis layer, data output layer.Method includes: based on three levels of linkage parameterization modeling to build simulation working condition;Execute multi-mode distribution calculation;Track furnace charge particle trajectory and execute standardized layer structure analysis, generate three-dimensional material surface, two-dimensional material line and radial ore coke ratio distribution data;The analysis result is visualized and exported.System built-in data import, geometry preprocessing, parameterized analysis, result generation and export four standard analysis processes, support offline scheme verification and online real-time monitoring, overcome the deficiencies of traditional tool module dispersion, parameter no linkage, low trajectory accuracy, analysis relies on artificial, and support closed-loop optimization decision.

Description

Technical Field

[0001] This invention belongs to the field of simulation and optimization technology of bell-less top charging operation in blast furnace ironmaking, specifically involving digital simulation, prediction and control technology of bell-less top charging operation in blast furnaces. Background Technology

[0002] Blast furnace ironmaking is a critical process in steel production, and the bell-less top charging operation is a core technological step for controlling the gas flow distribution within the furnace and ensuring stable furnace conditions. A precise charging system is essential for achieving efficient, low-consumption, and long-term operation of the blast furnace. For a long time, the formulation and adjustment of the charging system have relied heavily on the experience of operators, resulting in low efficiency, poor accuracy, and difficulty in quantitative evaluation. With the development of automation and intelligence in the metallurgical industry, using digital simulation technology to simulate, predict, and optimize the charging process has become an important technical means to improve blast furnace operation. Currently, various blast furnace charging simulation methods and systems have been proposed, achieving some progress in areas such as charging matrix optimization, particle motion modeling, and charge surface shape prediction. However, existing technologies still face a series of key technical bottlenecks when meeting the needs of industrial-grade precise verification and intelligent optimization.

[0003] In parametric modeling, traditional parameter systems are loosely structured, with physical property parameters, material distribution matrices, and material surface model parameters often set independently, lacking internal organic linkage and a unified verification mechanism, leading to distorted simulation results. Regarding particle trajectory tracking, existing technologies mostly employ simplified two-dimensional models, failing to adequately consider factors such as Coriolis force and centrifugal force, making it difficult to accurately characterize the three-dimensional behavior of furnace charge particles, such as the complex motion within the chute and the projectile motion in the furnace throat void. In terms of material distribution calculation modes, existing simulation software primarily focuses on forward simulation prediction, i.e., calculating the material surface result given material distribution parameters, lacking the ability to reverse-engineer the material distribution parameters with the desired material surface as the target, and has not yet constructed a closed-loop iterative decision-making framework combining forward simulation and reverse optimization. In terms of system architecture, the parameter configuration, trajectory calculation, visualization, and result analysis modules of existing material distribution simulation systems are relatively isolated, requiring manual import and export of data between different modules, resulting in fragmented processes and a lack of an integrated collaborative working mechanism. In terms of bed structure analysis, traditional methods rely on manual extraction of cross sections and measurement calculations from three-dimensional data. This process is cumbersome and highly subjective, making it difficult to quickly obtain quantitative analysis indicators such as radial coke ratio.

[0004] In summary, current blast furnace charging simulation technology still has significant technical gaps in terms of the systematic nature of parametric modeling, the refinement of trajectory tracking, the closed-loop nature of computational decision-making, the integration of system architecture, and the automation of result analysis. Therefore, there is an urgent need for an integrated, hierarchical, and automated blast furnace charging simulation method and system. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide an integrated simulation method and system for top charging of blast furnaces without bells, so as to solve the technical problems in the prior art, such as simulation distortion caused by lack of linkage verification in parameter modeling, insufficient trajectory tracking accuracy and lack of reverse optimization closed-loop decision-making, and isolated simulation system modules and low degree of automation in material layer analysis.

[0006] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides an integrated simulation method for bell-less blast furnace top charging, comprising the following steps: S1. Three-level linkage parametric modeling: Configure basic physical property parameters, material distribution matrix, and material surface evolution parameters, and ensure the consistency of the three-level parameter modeling through internal logical association and unified verification mechanism to construct a complete material distribution simulation working condition model; the unified verification mechanism includes: verifying the rationality of the material distribution equipment parameters based on the blast furnace geometric dimensions constraint, verifying the completeness of the parameters based on the range constraint of physical property parameters, and verifying the validity of the matrix based on the row and column consistency constraint of the material distribution matrix. The three-level verification results are linked and fed back, and if any verification fails, a correction is prompted. S2. Multi-mode charging calculation: Based on the proprietary blast furnace charging core algorithm, the simulation working condition model is solved and calculated. The calculation includes forward simulation mode and reverse optimization mode. The proprietary blast furnace charging core algorithm decomposes the movement of the furnace charge from the throttle valve to the charge surface into five consecutive physical stages: valve opening drop, chute collision, composite motion within the chute, projectile motion in the furnace throat void, and charge surface accumulation calculation. Mechanical modeling and numerical solution are performed for these stages. The forward simulation mode predicts the charge surface shape based on the given charging matrix. The reverse optimization mode uses the desired charge surface as the target and calls the forward simulation module to perform closed-loop iterative optimization. S3. Trajectory Tracking and Distribution Analysis: Track the movement trajectory of the furnace charge particles from the throttle valve to the charge surface, and execute a standardized material layer structure analysis process. The analysis process divides the inner diameter of the blast furnace into five equal parts in the radial direction, and respectively counts the ore-coke ratio of the center, inner side, platform section, outer side and edge, and outputs analytical data including three-dimensional material surface shape, two-dimensional radial material line and radial ore-coke ratio distribution. S4. Visualization and Data Output: The analytical data obtained in S3 is displayed graphically, and the simulation results and calculation process logs can be exported in a specified format. It supports two operating modes: offline scheme verification and online real-time monitoring. In the online real-time monitoring mode, the chute inclination angle, number of turns, and material line depth signals are read in real time through the industrial interface. The material placement simulation module is dynamically driven to perform synchronous modeling and calculation correction to generate real-time material placement trajectory and three-dimensional material surface, realizing digital twin monitoring.

[0007] Furthermore, the three-level parameters configured in the three-level linkage parametric modeling in S1 specifically include: The basic physical property parameters layer is entered in matrix form, including bulk density, particle density, friction coefficient, and collision recovery coefficient. The fabric control parameter layer defines the chute inclination angle and the number of fabric loops in the form of a fabric matrix, with each row and column number being strictly consistent. The material surface evolution parameter layer includes the inner angle of repose, outer angle of repose, material surface diffusion coefficient, and material packing coefficient.

[0008] Furthermore, the reverse optimization mode in S2 includes: taking the desired material surface as the objective, setting the chute inclination angle and the number of material loops in the material distribution matrix as optimization variables, using an intelligent optimization algorithm to iteratively call the forward simulation module, taking the minimization of the difference between the simulated material surface and the desired material surface as the objective function, taking the root mean square error between the simulated material surface and the desired material surface as the fitness function, and taking the chute inclination angle change not exceeding ±3° as the constraint condition to perform closed-loop iterative optimization, and outputting the optimal material distribution matrix; the intelligent optimization algorithm is selected from at least one of genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm and differential evolution algorithm.

[0009] Furthermore, the forward simulation mode in S2 is based on the autonomous blast furnace charging core algorithm to form a progressive calculation framework from single particle to whole circle charging, from single gear to multiple gears, and from single batch to multiple batches. It includes six calculation modes: chute critical inclination angle calculation, single particle motion trajectory calculation, single ring charging process calculation, inter-ring charging process calculation, single type of furnace charge multi-ring charging calculation, and multiple types of furnace charge multi-batch charging calculation.

[0010] Furthermore, the standardized material layer structure analysis process in S3 specifically includes the following four steps: Data import: Import 3D material surface data or material flow landing point data; Geometric preprocessing includes extracting two-dimensional material lines at arbitrary azimuth angles from a three-dimensional material surface, or reconstructing a three-dimensional material surface from multiple two-dimensional material lines through radial basis function interpolation. Parametric analysis: Set the radial statistical radius and layer thickness to automatically calculate the radial layer thickness distribution, volume distribution, mass distribution, and radial ore-coke ratio; Results generation and export: Generate CSV statistical reports and visualization charts.

[0011] Furthermore, the visualization and data output in S4 include: displaying the radial material line shape and thickness distribution in the form of a two-dimensional curve, displaying the material surface shape in the form of a three-dimensional surface, and exporting the simulation result data and calculation process log in a specified format.

[0012] Furthermore, the online real-time monitoring mode includes: reading the chute inclination angle, number of cycles, and material line depth signals in real time at a set frequency through an industrial interface; automatically triggering a rapid simulation for each new set of signals received; calculating only the material surface increment of the current cycle and updating the three-dimensional material surface; and issuing a model deviation prompt and saving the simulation data and logs for the deviation period when the deviation prediction of the material surface and the measured value of the probe exceed a preset threshold.

[0013] Secondly, the present invention provides a fabric integrated simulation system for implementing the above method, comprising: The data source and interface layer is used to access the factory's real-time database through industrial interfaces, while managing the local parameter database and historical data files. The core functionality management layer provides a unified graphical user interface, responsible for user login, permission management, navigation and scheduling of various functional modules, and provides an operating environment for method execution; The core simulation and calculation layer includes a parameter configuration module, a cloth simulation module, and a cloth optimization module. The parameter configuration module is used to configure and verify the three-level parameters. The cloth simulation module performs forward simulation, and the cloth optimization module performs reverse optimization and calls the cloth simulation module to form a closed loop. The post-processing and analysis layer includes a material surface / line processing module and a material layer structure analysis module, which are used to perform trajectory tracking and material layer structure analysis. The data output layer is used to visualize and export the calculation and analysis results.

[0014] Furthermore, the data source and interface layer are connected to the factory's real-time database via OPC or DBLink, supporting online real-time monitoring mode. In this mode, the data source and interface layer read the chute inclination angle, number of cycles, and material line depth signals in real time, dynamically driving the material placement simulation module to perform synchronous modeling and calculation correction, generating real-time material placement trajectory and three-dimensional material surface. The material placement simulation module includes six modes: chute critical inclination angle calculation, single particle motion trajectory calculation, single-ring material placement process calculation, inter-ring material placement process calculation, single-type furnace charge multi-ring material placement calculation, and multi-type furnace charge multi-batch material placement calculation. The material layer structure analysis module has a built-in four-step standardized analysis process, including data import, geometric preprocessing, parametric analysis, and result generation and export.

[0015] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method.

[0016] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention adopts an integrated five-layer architecture with a clear logic for the data source and interface layer, core function management layer, core simulation and calculation layer, result post-processing and analysis layer, and data output layer. The data flow between modules is automatically connected, and no manual intervention is required from parameter configuration to result analysis, thus improving work efficiency. At the same time, the standardized interface facilitates the future integration of high-fidelity models such as DEM and CFD-DEM, as well as the interface with factory MES and ERP systems. Compared with existing fabric simulation systems, it has a higher degree of integration and stronger scalability.

[0018] This invention employs a three-level linkage parametric modeling approach. The basic physical property parameters, material distribution matrix, and material surface evolution parameters are integrated through internal logical association and a unified verification mechanism, thus solving the problem of simulation result distortion caused by isolated parameters and lack of linkage verification. Furthermore, the motion of the furnace charge from the throttle valve to the material surface is decomposed into five continuous physical stages: valve orifice descent, chute collision, composite motion within the chute, projectile motion in the furnace throat void, and material surface accumulation calculation. These stages are then used for mechanical modeling and numerical solution. Compared with two-dimensional simplified models and particle models, this invention improves the accuracy of particle trajectory and material surface prediction.

[0019] This invention supports two core modes: forward simulation prediction and reverse optimization decision-making. Through multi-mode material distribution calculation and closed-loop iteration framework, the optimal material distribution matrix can be automatically calculated based on the desired material surface, realizing the leap from experience-based decision-making to model-based intelligent decision-making. The standardized four-step analysis process, combined with radial five-part ore-coke ratio statistics, shortens the traditional manual analysis that takes several hours to the second level, and quickly extracts quantitative indicators such as radial ore-coke ratio and material layer thickness distribution.

[0020] This invention supports two operating modes: offline solution verification and online real-time monitoring. The offline mode is used for pre-research and verification of material placement solutions, while the online mode uses an industrial interface to read the chute inclination angle, number of turns, and material line depth signals in real time to drive synchronous simulation, thereby achieving real-time monitoring based on digital twins and meeting the needs of different industrial scenarios.

[0021] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0022] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a schematic diagram of the five-layer logic architecture of the bellless blast furnace top charging integrated simulation system provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the standardized analysis process for the material layer structure provided in an embodiment of the present invention; Figure 3 A schematic diagram of the forward simulation and reverse optimization closed-loop decision-making framework provided in the embodiments of the present invention.

[0023] in, Figure 1 In this diagram, S110 represents the data source and interface layer, S120 represents the core function management layer, S130 represents the core simulation and calculation layer, S140 represents the result post-processing and analysis layer, and S150 represents the data output layer. Figure 2 In the diagram, 21, 22, 23, and 24 represent the four processing stages: data import, geometric preprocessing, parametric analysis, and result generation and export, respectively. Figure 3 In the diagram, 31 represents the forward simulation process, and 32 represents the reverse optimization process. 31 and 32 form a closed-loop iterative relationship through a cyclic arrow. Detailed Implementation

[0024] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0025] Example 1 like Figure 1 As shown, the blast furnace bell-less top charging integrated simulation system described in this embodiment adopts a five-layer logical architecture, including a data source and interface layer S110, a core function management layer S120, a core simulation and calculation layer S130, a result post-processing and analysis layer S140, and a data output layer S150.

[0026] The data source and interface layer S110 is responsible for accessing basic production data and interacting with external systems such as MES. It accesses the factory's real-time database through industrial interfaces such as OPC and DBLink, and manages the local parameter database and historical data files. The core function management layer S120 provides a unified graphical user interface, enabling the configuration of blast furnace operating parameters and the scheduling of simulation tasks. It is responsible for user login, permission management, and navigation and scheduling of various functional modules. The core simulation and calculation layer S130 includes a parameter configuration module, a material distribution simulation module, and a material distribution optimization module. It executes the core material distribution algorithm and simulates the material distribution in the furnace. The result post-processing and analysis layer S140 visualizes the simulation results and performs multi-dimensional data analysis, including a material surface / line processing module and a material layer structure analysis module. The data output layer S150 generates optimization decision suggestion reports and supports data report export and sharing. The above five layers work collaboratively according to the data flow from S110 to S150, forming a fully integrated link from data access, task scheduling, simulation calculation, result analysis to data output.

[0027] Example 2 Based on the above system, this invention also provides an integrated simulation method for bell-less blast furnace top charging, comprising the following steps: S1, Three-level linkage parametric modeling.

[0028] Specifically, the basic physical property parameter layer records bulk density, particle density, friction coefficient, and collision recovery coefficient in matrix form; the material distribution control parameter layer defines the chute inclination angle and the number of distribution rings in the form of a distribution matrix, with strict consistency in the number of rows and columns; the material surface evolution parameter layer includes inner angle of repose, outer angle of repose, material surface diffusion coefficient, and material packing coefficient. These three layers of parameters form a parameter-driven simulation whole through internal system logic and undergo consistency verification through a unified verification mechanism: verifying the rationality of the material distribution equipment parameters based on blast furnace geometry constraints, verifying the completeness of the parameters based on physical property parameter range constraints, and verifying the validity of the matrix based on the consistency constraints of the distribution matrix rows and columns; the verification results of the three layers are linked and fed back, prompting correction if any verification fails. After successful verification, a complete material distribution simulation working condition model is constructed.

[0029] S2, Multi-mode fabric calculation.

[0030] The core algorithm for autonomous blast furnace charging decomposes the movement of the charge from the throttle valve to the charge surface into five consecutive physical stages: falling from the valve orifice, collision in the chute, combined motion within the chute, projectile motion in the throat void, and calculation of charge accumulation on the charge surface. Mechanical modeling and numerical solutions are then performed for each stage. The forward simulation mode predicts the charge surface shape based on a given charging matrix. like Figure 3As shown in Figure 31, the forward simulation process sequentially executes parameter initialization, trajectory calculation, landing point determination, accumulation contour construction, volume verification, and material surface shape update. It then iteratively solves for multiple material placement levels and batches using multi-mode material placement calculations and a bisection method. The reverse optimization mode targets the desired material surface, as shown in Figure 31. Figure 3 As shown in Figure 32, the optimization objective is set sequentially, optimization variables are selected, constraints are defined, the optimization algorithm is executed and the predictor is called, the optimization result is output, and then the optimization iteration returns to the forward simulation process 31, forming a closed-loop iterative optimization. Among them, the material distribution simulation module includes six calculation modes: chute critical inclination angle calculation, single particle motion trajectory calculation, single-ring material distribution process calculation, inter-ring material distribution process calculation, single type of furnace charge multi-ring material distribution calculation, and multiple types of furnace charge multi-batch material distribution calculation.

[0031] S3, Trajectory Tracking and Distribution Analysis.

[0032] The system tracks the movement trajectory of the furnace charge particles from the throttling valve to the charge surface, and the charge layer structure analysis module executes a standardized charge layer structure analysis process. For example... Figure 2 As shown, the process includes four steps in sequence: data import, geometric preprocessing, parametric analysis, and result generation and export. Data import is used to import three-dimensional material surface data or material flow landing point data; geometric preprocessing is used to extract two-dimensional material lines at arbitrary azimuth angles from the three-dimensional material surface, or to reconstruct the three-dimensional material surface from multiple two-dimensional material lines through radial basis function interpolation; parametric analysis is used to set the radial statistical radius and layer thickness, and automatically calculate the radial material layer thickness distribution, volume distribution, mass distribution, and radial ore-coke ratio; result generation and export is used to generate CSV statistical reports and visualization charts. Radially, the blast furnace inner diameter is divided into five equal parts, and the ore-coke ratio is statistically analyzed at the center, inner side, platform section, outer side, and edge, outputting analytical data including the three-dimensional material surface shape, two-dimensional radial material lines, and radial ore-coke ratio distribution.

[0033] S4. Visualization and Data Output.

[0034] The system graphically displays the analytical data obtained from S3, showing the radial material line shape and thickness distribution as a two-dimensional curve and the material surface shape as a three-dimensional surface. It also supports exporting simulation results and calculation logs in a specified format. This method supports both offline scheme verification and online real-time monitoring modes. In online real-time monitoring mode, the system reads the chute inclination angle, number of turns, and material line depth signals in real time through an industrial interface, dynamically driving the material placement simulation module to perform synchronous modeling and calculation corrections, generating real-time material placement trajectory and a three-dimensional material surface, thus achieving digital twin monitoring.

[0035] Example 3 This embodiment provides two typical application scenarios for the described method and system: Example 1 is offline solution verification and material layer analysis, and Example 2 is online real-time monitoring and optimization. These examples are only for illustrating the implementation of the present invention and are not intended to limit the scope of protection of the present invention.

[0036] Example 1: Offline Solution Validation and Material Layer Analysis A blast furnace process engineer needs to verify a new charging matrix, with four levels for both coke and ore. The operation procedure is as follows: 1. System Startup and Parameter Configuration: After logging in, enter the main interface and click "Parameter Configuration" in the left navigation tree. Enter the blast furnace geometry dimensions, such as throat radius 4.6m and chute length 4.1m. Select coke and sinter as the burden type, enter physical property parameters such as density and friction coefficient, enter the burden distribution matrix composed of the inclination angle sequence and the number of cycles, and set the burden surface evolution parameters. The system will perform a unified verification according to S1. After displaying "Parameter verification passed," it will construct the burden distribution simulation model.

[0037] 2. Perform forward simulation: Click the "Burning Simulation" module, select "Offline Mode," and click "Start Calculation." The system calls the core algorithm at S2 to calculate the furnace charge trajectory, material flow stacking shape, and material layer structure update. The calculation is completed in about 5 seconds, and a 3D material surface map is displayed in the central work area.

[0038] 3. Material Layer Structure Analysis: Click "Material Layer Structure Analysis," import the previously generated 3D material surface data, set the radial range to 0 to 3.2m and the step size to 0.1m, and click "Analyze." The system generates the radial thickness curve and ore-coke ratio curve within 2 seconds using S3; the engineer found that the ore-coke ratio rose sharply at a radius of 2.5m, indicating that the edge load was too high.

[0039] 4. Reverse Optimization: Click the "Fabric Optimization" module, set the ideal ore-coke ratio distribution curve, select the particle swarm optimization algorithm, population size 30, and iteration 50 generations. The system calls the forward simulation module in the background to perform closed-loop optimization according to the S2 reverse optimization mode. After 2 minutes, it outputs the recommended fabric matrix adjustment scheme, suggesting reducing the number of outer rings by 1 and increasing the inner ring inclination angle by 2°.

[0040] 5. Export Report: Click "Export", and the system will package the curve graph, CSV data table and current operating parameters into a ZIP file using S4 for reporting.

[0041] Example 2: Online Real-Time Monitoring and Optimization During blast furnace production, the system operates in online mode: 1. Data Access: The data source and interface layer S110 reads signals such as chute inclination angle, number of turns, and material line depth from the PLC in real time through the OPC server, with a refresh rate of 1Hz.

[0042] 2. Real-time simulation: The fabric simulation module automatically triggers a fast simulation every time it receives a new set of signals, calculates only the material surface increment of the current ring, updates the three-dimensional material surface display, and realizes digital twin monitoring.

[0043] 3. Anomaly warning: When the deviation between the material surface predicted by the simulation and the actual value measured by the probe exceeds 10%, the system will pop up a message saying "The model deviation is too large. It is recommended to recalibrate the parameters" and automatically save all simulation data and logs for the period of deviation.

[0044] 4. Online Optimization: If the operator detects an abnormal gas flow distribution and suspects an unreasonable material distribution matrix, they can click the "Material Distribution Optimization" module, set the desired material surface to a platform with a small slope, select the differential evolution algorithm, set the population size to 30, and iterate for 50 generations. The system will then call the forward simulation module in the background to perform closed-loop optimization, outputting a recommended new material distribution matrix after 2 minutes. After operator confirmation, the parameters will be sent to the PLC for execution.

[0045] Furthermore, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described integrated simulation method for bell-less top charging of a blast furnace. The present invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method.

[0046] Through the above description of specific implementation methods, those skilled in the art can understand and reproduce the integrated simulation method and system for blast furnace bell-less top charging described in this invention: This method organically integrates parametric modeling, high-precision trajectory tracking, multi-mode closed-loop calculation, and automated material layer analysis into a five-layer integrated architecture through S1 three-level linkage parametric modeling, S2 multi-mode charging calculation, S3 trajectory tracking and distribution analysis, and S4 visualization and data output, realizing a complete technical chain for blast furnace charging from offline scheme verification to online decision support.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A simulation method for integrated top charging of a bell-less blast furnace, characterized in that, Includes the following steps: S1. Three-level linkage parametric modeling: Configure basic physical property parameters, cloth matrix and material surface evolution parameters, and ensure the modeling consistency of the three-level parameters through the internal logical association and unified verification mechanism of the system to build a complete cloth simulation working condition model. The unified verification mechanism includes: verifying the rationality of the charging equipment parameters based on the blast furnace geometric dimensions, verifying the completeness of the parameters based on the range constraints of the physical property parameters, and verifying the validity of the matrix based on the row and column consistency constraints of the charging matrix. The three-layer verification results are linked and fed back, and if any verification fails, a correction is prompted. S2. Multi-mode charging calculation: Based on the independent blast furnace charging core algorithm, the simulation working condition model is solved and calculated. The calculation includes forward simulation mode and reverse optimization mode. The core algorithm for autonomous blast furnace charging decomposes the movement of the charge from the throttle valve to the charge surface into five consecutive physical stages: falling from the valve orifice, collision in the chute, compound motion within the chute, projectile motion in the void of the furnace throat, and calculation of charge accumulation on the charge surface. The algorithm then performs mechanical modeling and numerical solution. The forward simulation mode predicts the shape of the material surface based on a given fabric matrix; The reverse optimization mode takes the desired material surface as the target and calls the forward simulation module to perform closed-loop iterative optimization. S3. Trajectory Tracking and Distribution Analysis: Track the movement trajectory of the furnace charge particles from the throttle valve to the charge surface, and execute a standardized material layer structure analysis process. The analysis process divides the inner diameter of the blast furnace into five equal parts in the radial direction, and respectively counts the ore-coke ratio of the center, inner side, platform section, outer side and edge, and outputs analytical data including three-dimensional material surface shape, two-dimensional radial material line and radial ore-coke ratio distribution. S4. Visualization and Data Output: The analytical data obtained in S3 is displayed graphically, and the simulation results data and calculation process logs are exported in a specified format. It supports two operating modes: offline solution verification and online real-time monitoring. In the online real-time monitoring mode, the chute inclination angle, number of turns, and material line depth signals are read in real time through the industrial interface. The material placement simulation module is dynamically driven to perform synchronous modeling and calculation correction to generate real-time material placement trajectory and three-dimensional material surface, thereby realizing digital twin monitoring.

2. The method according to claim 1, characterized in that, The three-level parameters configured in the three-level linkage parametric modeling in S1 specifically include: The basic physical property parameters layer is entered in matrix form, including bulk density, particle density, friction coefficient, and collision recovery coefficient. The fabric control parameter layer defines the chute inclination angle and the number of fabric loops in the form of a fabric matrix, with each row and column number being strictly consistent. The material surface evolution parameter layer includes the inner angle of repose, outer angle of repose, material surface diffusion coefficient, and material packing coefficient.

3. The method according to claim 1, characterized in that, The reverse optimization mode in S2 includes: taking the desired material surface as the objective, setting the chute inclination angle and the number of material loops in the material distribution matrix as optimization variables, using an intelligent optimization algorithm to iteratively call the forward simulation module, taking the minimization of the difference between the simulated material surface and the desired material surface as the objective function, the root mean square error between the simulated material surface and the desired material surface as the fitness function, and using the constraint that the change in the chute inclination angle does not exceed ±3° to perform closed-loop iterative optimization, and outputting the optimal material distribution matrix; the intelligent optimization algorithm is selected from at least one of genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm and differential evolution algorithm.

4. The method according to claim 1, characterized in that, The forward simulation mode in S2 is based on the autonomous blast furnace charging core algorithm to form a progressive calculation framework from single particle to whole circle charging, from single gear to multiple gears, and from single batch to multiple batches. It includes six calculation modes: chute critical inclination angle calculation, single particle motion trajectory calculation, single ring charging process calculation, inter-ring charging process calculation, single type of furnace charge multi-ring charging calculation, and multiple types of furnace charge multi-batch charging calculation.

5. The method according to claim 1, characterized in that, The standardized material layer structure analysis process in S3 specifically includes the following four steps: Data import: Import 3D material surface data or material flow landing point data; Geometric preprocessing includes extracting two-dimensional material lines at arbitrary azimuth angles from a three-dimensional material surface, or reconstructing a three-dimensional material surface from multiple two-dimensional material lines through radial basis function interpolation. Parametric analysis: Set the radial statistical radius and layer thickness to automatically calculate the radial layer thickness distribution, volume distribution, mass distribution, and radial ore-coke ratio; Results generation and export: Generate CSV statistical reports and visualization charts.

6. The method according to claim 1, characterized in that, The visualization and data output in S4 include: displaying the radial material line shape and thickness distribution in the form of a two-dimensional curve, displaying the material surface shape in the form of a three-dimensional curved surface, and exporting the simulation result data and calculation process log in a specified format.

7. The method according to claim 1, characterized in that, The online real-time monitoring mode includes: reading the chute inclination angle, number of cycles, and material line depth signals in real time at a set frequency through an industrial interface; automatically triggering a fast simulation for each new signal received; calculating only the material surface increment of the current cycle and updating the three-dimensional material surface; when the deviation between the simulated predicted material surface and the probe measured value exceeds a preset threshold, issuing a model deviation prompt and saving the simulation data and logs for the deviation period.

8. A fabric integrated simulation system for implementing the method according to any one of claims 1 to 7, characterized in that, include: The data source and interface layer is used to access the factory's real-time database through industrial interfaces, while managing the local parameter database and historical data files. The core functionality management layer provides a unified graphical user interface, responsible for user login, permission management, navigation and scheduling of various functional modules, and provides an operating environment for method execution; The core simulation and calculation layer includes a parameter configuration module, a cloth simulation module, and a cloth optimization module. The parameter configuration module is used to configure and verify the three-level parameters. The cloth simulation module performs forward simulation, and the cloth optimization module performs reverse optimization and calls the cloth simulation module to form a closed loop. The post-processing and analysis layer includes a material surface / line processing module and a material layer structure analysis module, which are used to perform trajectory tracking and material layer structure analysis. The data output layer is used to visualize and export the calculation and analysis results.

9. The system according to claim 8, characterized in that, The data source and interface layer are connected to the factory's real-time database via OPC or DBLink, supporting online real-time monitoring mode; In the online real-time monitoring mode, the chute inclination angle, number of turns, and material line depth signals are read in real time through the data source and interface layer, and the material simulation module is dynamically driven to perform synchronous modeling and calculation correction to generate real-time material trajectory and three-dimensional material surface. The material feeding simulation module includes six modes: chute critical inclination angle calculation, single particle motion trajectory calculation, single-ring material feeding process calculation, inter-ring material feeding process calculation, single type of furnace charge multi-ring material feeding calculation, and multiple furnace charge multi-batch material feeding calculation. The material layer structure analysis module has a built-in four-step standardized analysis process, including data import, geometric preprocessing, parametric analysis, and result generation and export.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

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