Method for collaborative optimization analysis of virtual and actual systems of steelmaking continuous casting production organization

Abstract

The invention relates to a collaborative optimization analysis method for virtual and actual systems of a steelmaking continuous casting production organization, and belongs to the technical field of intelligent manufacturing of steel production. The method aims at solving the problems of scheduling lag, low equipment utilization rate, low steel ladle turnover efficiency, frequent crown block conflicts and the like caused by the fact that traditional production organizations depend on artificial experience. According to the technical scheme, a virtual-real fused logistics simulation platform is constructed through a data acquisition layer, a data analysis layer, a simulation modeling layer, an analysis optimization layer and a control application layer, actual production data and a simulation model are integrated, and a production organization and a scheduling plan are cooperatively optimized by adopting an operation rule and an intelligent algorithm. And multi-dimensional acquisition, analysis and dynamic regulation and control of material flow, temperature, time and production mode in the whole steelmaking continuous casting process are realized. According to the invention, real-time acquisition, dynamic scheduling and visual management and control of the production process can be realized, a trial and error link is transferred from a physical entity to a virtual space, and the experiment cost and risk are significantly reduced.

Description

Technical Field

[0001] This invention belongs to the field of intelligent manufacturing technology in steel production, and relates to a method for collaborative optimization analysis of virtual and real systems in steelmaking continuous casting production organization. Background Technology

[0002] In the steel manufacturing industry, the continuous casting process is a complex industrial system involving multiple processes and equipment operating in coordination. This process not only includes major steps such as converter or electric furnace smelting, refining, and continuous casting, but also involves the transportation and handling of high-temperature molten materials such as molten steel and iron, as well as the frequent scheduling of critical equipment such as cranes. Traditional production organization models heavily rely on the manual experience of scheduling personnel, which often proves inadequate in the face of such a complex and dynamic environment.

[0003] This scheduling method, primarily based on human experience, has several inherent drawbacks. First, various data within the production system, such as process times, molten steel temperatures, and equipment operating status, are typically scattered across different subsystems, creating information silos. This makes it difficult for schedulers to obtain comprehensive, real-time production information to make optimal decisions. Second, manual scheduling suffers from delayed responses, making it difficult to handle unexpected situations on the production floor, such as temporary equipment failures or sudden changes in production rhythm. This can easily lead to excessively long waiting times between processes and production interruptions such as continuous casting machine shutdowns. These problems directly result in low equipment utilization, increased energy consumption, and high production costs.

[0004] Furthermore, the turnover efficiency of key resources, such as the heat turnover rate of ladles and slag pots, has a significant impact on production energy consumption and rhythm stability. However, in the traditional model, their scheduling paths are difficult to optimize. At the same time, the operating areas of multiple cranes in the workshop often overlap, and manual scheduling can easily lead to equipment conflicts, posing safety hazards and reducing logistics efficiency. Molten steel experiences temperature drops during transportation and waiting between processes; unstable production rhythms pose challenges to temperature control and may affect the final product quality.

[0005] While existing manufacturing execution systems (MES) have improved management efficiency to some extent, their functions are mainly focused on data recording and static production scheduling, lacking the ability to dynamically simulate the entire process, make real-time predictions, and perform multi-objective collaborative optimization. The inability to virtually verify and optimize production plans before execution means that significant trial-and-error costs and risks must still be borne in the actual production process.

[0006] Therefore, there is an urgent need in this field for a new method that can deeply integrate actual production with virtual simulation, achieve real-time data-driven operation, and perform multi-dimensional collaborative optimization analysis, in order to systematically solve the above problems and ultimately significantly improve the intelligence level and operational efficiency of steelmaking continuous casting production organization. Summary of the Invention

[0007] In view of this, the purpose of this invention is to provide a method for collaborative optimization analysis of the virtual and real systems in steelmaking continuous casting production organization.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A method for collaborative optimization analysis of the virtual and real systems in steelmaking continuous casting production organization includes the following steps: S1: Collect material flow sample data of the entire steelmaking-continuous casting process through the data acquisition layer; S2: Classify and analyze the material flow sample data through the data analysis layer to establish a process analysis data analysis module; S3: Through the simulation modeling layer, a virtual simulation model of the steelmaking-continuous casting process, a virtual turnover model of the ladle / slag pot, and a simulation optimization planning and scheduling model are constructed, and a virtual-real integrated logistics simulation platform is built. S4: Perform multi-objective optimization analysis and bottleneck identification on the simulation results output by the virtual simulation model through the analysis and optimization layer; S5: Enables visualized monitoring of production data, dynamic scheduling command issuance, and closed-loop optimization through the control application layer.

[0009] Furthermore, in S1, the material flow sample data includes at least one of the following: process time parameters, molten steel temperature parameters, steel grade production mode, crane equipment parameters, ladle operation time parameters, and slag pot operation time parameters.

[0010] Furthermore, in S2, the process analysis data parsing module includes at least one of the following: process time parameter parsing module, molten steel temperature parameter parsing module, steel grade production mode parsing module, crane equipment parameter parsing module, ladle operation parameter parsing module, and slag pot operation parameter parsing module.

[0011] Furthermore, in S3, the simulation modeling layer uses a logistics simulation software platform to construct a multi-process operation simulation model and integrates a simulation optimization planning and scheduling platform to collaboratively optimize production planning and scheduling through "operation rules + intelligent algorithms".

[0012] Furthermore, the logistics simulation software platform is the Plant Simulation software platform, which contains various types of module sets for custom processes / devices. These module sets include at least one of the following: Crane module set, Size Control module set, Ladle Furnace (LF) module set, Basic Oxygen Furnace (LD) module set, Continuous Caster (CC) module set, Ruhrstahl-Heraeus (RH) module set, Desulfurization (TS) module set, Vacuum Degassing (VD) module set, Electric Arc Furnace (EAF) module set, Torpedo Car Station (TPS) module set, and Blast Furnace (BF) module set.

[0013] Furthermore, the "operation rules" include basic production scheduling rules, time control rules, equipment matching rules, and manual adjustment rules; the "intelligent algorithms" include genetic algorithms, heuristic algorithms, or dynamic programming algorithms, used to optimize with the goals of maximizing output, maximizing equipment utilization, and maximizing ladle turnover.

[0014] Furthermore, S4 specifically includes: performing multi-objective optimization analysis on the simulation results, identifying bottleneck processes, calculating laminar flow operation index, optimizing ladle and slag pot turnover, analyzing and resolving crane scheduling conflicts, and optimizing molten steel temperature-time coordination.

[0015] Furthermore, the laminar flow operation index is used to quantitatively evaluate the continuous operation level of the continuous casting machine.

[0016] A collaborative optimization analysis system for the virtual and real systems of steelmaking continuous casting production organization for implementing the method, comprising: The data acquisition layer and the data analysis layer together constitute the statistical data analysis system for the steelmaking-continuous casting process; The simulation modeling layer and the analysis and optimization layer together constitute a steelmaking-continuous casting virtual simulation system. The control application layer constitutes a visual display and control system.

[0017] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method.

[0018] The beneficial effects of this invention are as follows: (1) This invention constructs a virtual-real integrated logistics simulation platform, deeply integrating actual production data with simulation models, to achieve multi-dimensional real-time acquisition and analysis of the entire steelmaking continuous casting process. This transforms the production organization mode from reactive processing relying on manual experience to real-time prediction and dynamic scheduling based on data-driven approaches. The system can predict future production status and identify potential problems in advance, thereby achieving precise control and forward-looking decision support for the production process.

[0019] (2) Through the coordinated optimization of operating rules and intelligent algorithms, this method can systematically improve the utilization efficiency of key equipment and resources. It effectively optimizes production planning and scheduling instructions, reduces waiting time between processes, and ensures continuous and efficient operation of the continuous casting machine. At the same time, by optimizing the turnover path of key containers such as ladles and slag pots, its hot turnover rate is significantly improved, thereby improving the logistics efficiency and rhythm stability of the entire production system.

[0020] (3) The core advantage of this method lies in shifting the trial-and-error process from physical entities to virtual spaces. Before carrying out actual equipment modifications, process adjustments, or production plan changes, sufficient simulation exercises and effect verification can be conducted on a virtual simulation platform. This greatly reduces experimental costs, energy consumption, and production risks caused by immature solutions, providing a safe and economical verification environment for enterprises' technological transformation and production optimization.

[0021] (4) The system has the ability to sense process deviations in real time and automatically trigger dynamic adjustments. When sudden situations such as abnormal temperature or equipment delays occur on the production site, the system can respond quickly, recalculate optimization, and issue adjustment instructions. This closed-loop optimization mechanism significantly enhances the flexibility and robustness of the production system in the face of interference, ensuring the smooth operation of the production process and the consistency and stability of product quality.

[0022] (5) By controlling the application layer, the system presents complex production data, simulation results, and optimization instructions to managers in an intuitive way, such as 3D virtual walkthroughs and real-time data dashboards. This high degree of visualization greatly improves the transparency of production management, enabling managers to grasp the overall operation of the workshop at a glance, thereby making more scientific and efficient decisions.

[0023] 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

[0024] 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 System architecture diagram for virtual-physical collaboration optimization in steelmaking-continuous casting workshop; Figure 2 A schematic diagram of the layout of a 3D model for a logistics simulation platform; Figure 3 The simulation-optimization interface diagram for the furnace scheduling algorithm is shown below. Figure 4 A schematic diagram of the turnover of molten iron ladles; Figure 5 This is a schematic diagram of the turnover of molten steel ladles; Figure 6 This is a schematic diagram of the molten iron ladle transfer process; Figure 7 This is a schematic diagram of the semi-steel ladle circulation process; Figure 8 This is a schematic diagram of the ladle circulation process; Figure 9 A 3D model diagram for the implementation case. Detailed Implementation

[0025] 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.

[0026] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0027] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0028] like Figure 1 As shown, the present invention aims to provide a collaborative optimization analysis method for the virtual and real systems of steelmaking continuous casting production organization. By constructing a multi-level virtual and real simulation optimization system, real-time prediction, dynamic scheduling and visual control of the production process can be realized, significantly improving the intelligence level and operating efficiency of steelmaking production organization.

[0029] The multi-level virtual-real collaborative optimization system mainly consists of a process statistical data analysis system and a steelmaking continuous casting virtual simulation system, further visualized through a visualization system. The entire system architecture comprises a data acquisition layer, a data analysis layer, a simulation modeling layer, an analysis and optimization layer, and a control application layer. Data Acquisition Layer: Also known as the perception layer, this layer is responsible for collecting real-time sample data related to the entire material flow analysis from physical entities. This data primarily includes process time parameters, molten steel temperature parameters, steel grade production modes, parameters of crane equipment in each span, ladle operating time parameters, and slag ladle operating time parameters. Details are as follows: 1) Process time parameters: Collect the operation time of each material handling stage in the converter process, argon station process, LF refining (or RH refining) process, and continuous casting process for different steel grades; 2) Molten steel temperature parameters: The temperature of molten steel entering and leaving the station in the converter, argon station, LF furnace refining (or RH refining) and continuous casting processes for different steel grades; 3) Steel Grade Production Mode: Statistics are compiled on the output of each unit / equipment within a specific period, and the products produced are categorized and statistically analyzed by steel grade. Simultaneously, a process path diagram of the steelmaking continuous casting workshop's production mode is drawn for this period. The diagram shows the production operation of the steelmaking continuous casting workshop more intuitively, with the arrow thickness proportional to the output of that process route. 4) Parameters of cranes in each span: Data and statistics are collected on the effective operating time, conflicting operating time, operating time, operating range of each crane, lifting / lowering time of steel ladles by cranes, time for adding molten iron by cranes, time for adding scrap steel by cranes, and time for pouring casting residue by cranes during a certain period. Simultaneously, a bar chart of the crane operating efficiency in the steelmaking continuous casting workshop is drawn for this period. 5) Ladle running time parameters: The data collection process includes the ladle travel time of the entire process of receiving molten steel from the converter → LF ladle refining → RH vacuum refining (if applicable) → continuous casting → casting residue → hot repair → ladle drying (if applicable) → receiving molten steel from the converter. The data must be accurate to the second. 6) Slag pot operating time parameters: The data collection process is based on the slag pot's travel time throughout the entire operation process, including the converter slag pot receiving slag from the first converter furnace → the second furnace slag pot → the third furnace slag pot → the fourth furnace slag pot (if any) → slag pit slagging → repair → receiving converter slag, etc. The data must be accurate to the second. Data Statistics / Analysis Layer: Establishes a process analysis and evaluation database, classifying and parsing the data collected by the perception layer. Details are as follows: 1) Process Time Parameter Analysis: Its main function is to classify and statistically analyze the production cycle of different units / processes in steelmaking and continuous casting by steel grade, and display it in the form of charts. Specifically, for the collected data on the operation time of each material processing stage in the converter process, argon station process, LF refining (or RH refining) process, and continuous casting process, it calculates the maximum (min), minimum (min), and average (min) values ​​for each material processing stage of each process for different steel grades, and displays them in the form of bar charts. 2) Steel Temperature Parameter Analysis: Its main function is to statistically analyze the differences in operating temperature drop caused by different process routes during the production of different steel grades, thereby summarizing the target control temperature of a certain steel grade under different process routes. Specifically, it statistically analyzes the temperature drop (°C / min) of molten steel entering and leaving the converter, argon station, LF furnace refining (or RH refining), and continuous casting processes for different steel grades, and displays it as a trend curve of the average temperature change during the production process of a certain steel grade. Furthermore, it displays the temperature range distribution from tapping to the continuous casting ladle using a bar chart. 3) Steel Grade Production Mode Analysis: Its main function is to calculate the relationship between the output of different units and factors such as production cycle, operating rate, and casting speed by setting and selecting parameters of converters and continuous casting machines. Specifically, it statistically analyzes the output distribution ratio of different steel grades produced by different process units, performs process output statistics (furnaces), analyzes the capacity of each process / unit in the steelmaking and continuous casting process, and generates graphs showing the relationship between the annual capacity of the converter process and product structure and calendar operating rate, the annual capacity of the continuous casting process and product structure and calendar operating rate, the relationship between the annual production capacity of each process under different steel grade ratios, and the relationship between the capacity of the steelmaking-continuous casting system and the steel grade ratio. 4) Crane Equipment Parameter Analysis: Its main function is to statistically analyze the daily operating time and operating area of ​​each span of the steelmaking continuous casting crane, thereby summarizing and calculating the maximum, minimum and average values ​​of the daily effective operation rate, daily conflict rate, daily operation rate, operating area of ​​each crane, lifting / lowering time, molten iron mixing time, scrap steel mixing time, and casting residue pouring time, and displaying the crane operating area in the form of charts. 5) Ladle Operation Parameter Analysis: Its main function is to classify and statistically analyze the cycle time of the two operation and turnover stages of full and empty ladles by steel type, and display it in the form of charts. Specifically, it calculates the minimum (s), maximum (s), and average (s) of each data item in the collected ladle operation process according to different steel types, and statistically analyzes the total ladle operation time (s), total flexible operation time (s), full ladle operation time (s), empty ladle operation time (s), percentage of full ladle operation time to total operation time (%), percentage of empty ladle operation time to total operation time (%), ladle online turnover rate, etc., and then summarizes and analyzes the relationship between product structure and ladle operation quantity, optimizing and reducing the "flexible time" of the operation process, thus preparing for the optimization of the ladle operation process.

[0030] 6) Slag Pot Operation Parameter Analysis: Its main function is to classify and statistically analyze various cycle time parameters of the slag pot operation cycle, thereby counting the number of online operations, optimizing and reducing the "flexible time" of the operation process, and preparing for the optimization of the slag pot operation process.

[0031] Simulation Modeling Layer: The Plant Simulation logistics simulation software platform is used to construct a multi-process operation simulation model of the steelmaking-continuous casting process that takes into account the operation of cranes in each span and the turnover constraints of steel ladles. This model can achieve accurate simulation and prediction of the actual production operations of multiple processes / units.

[0032] 1) Overall Modeling Approach: The steelmaking-continuous casting process differs from typical discrete manufacturing, exhibiting intermittent / quasi-continuous operation characteristics. It is a hybrid production process centered around the continuous casting machine, meaning that multiple heats must be cast consecutively during production. For production scheduling in the simulation model, the general approach is to first assign the continuous casting machine to each casting cycle within the production plan, and then, following the reverse production flow sequence, assign the refining furnace and converter to each heat cycle in turn, generating a heat cycle schedule. To ensure continuous casting in the simulation model, the material flow simulation under the constraint of continuous casting is achieved by reversing the steelmaking-continuous casting process.

[0033] 2) Simulation Model Construction The construction of a multi-process simulation model for steelmaking-continuous casting mainly includes the following three steps: (1) Based on the workshop layout of the steelmaking-continuous casting section, the process layout diagram is simply processed, that is, the overhead cranes and ground vehicles in the drawing are removed, and then the workshop process layout diagram is imported into the Plant Simulation simulation platform. Using the custom-defined three-dimensional simulation modules in Plant Simulation (Crane class module set, Size_Control class module set, LF class module set, LD class module set, CC class module set, RH class module set, TS class module set, VD class module set, EAF class module set, TPS class module set, BF class module set, etc.), the corresponding modules are dragged in, and the physical architecture of the simulation model is constructed according to the equipment and logistics information. The relevant parameters are set according to the meaning and function of the objects referred to by each process module and the overhead crane cross-module. For details of its modeling layout, see Figure 2 .

[0034] (2) Input the steel production mode (i.e. steel process path) of the casting plan and the material flow parameters of the data analysis layer, the processing time of different steel grades in each process / unit, the lifting point position of each process / unit, steel grade information, etc. into the simulation optimization plan scheduling model, and perform data collaborative interaction and transmission with the physical architecture of the simulation model of the Plant Simulation platform. The information tables such as the production furnace plan and the crane scheduling plan are transmitted to the Plant Simulation platform.

[0035] (3) In the simulation optimization planning and scheduling model software, the production scheduling model based on "operation rules + intelligent algorithm" is used to simulate and calculate the main production modes of several schemes in the steelmaking-continuous casting workshop production process, and optimize the waiting time and furnace-machine matching degree of the production process.

[0036] (4) Conduct simulation and pre-analysis of multiple schemes and scenarios on the Plant Simulation platform.

[0037] 3) Production planning and scheduling Production scheduling refers to optimizing the performance of a manufacturing system by rationally arranging tasks, processes, and other resources according to production plan requirements and under the premise of meeting production constraints. To further improve the efficiency of production scheduling and its feasibility in real-world environments, this paper constructs a production planning rule base, including furnace-time planning and casting-time planning models, based on the analysis and optimization of basic material flow parameters in the steelmaking-continuous casting process. This leads to the development of optimal furnace-time planning and optimal casting-time planning models to meet the actual needs of steelmaking production scheduling in the context of intelligent manufacturing and improve the usability of the models and algorithms.

[0038] For different production plans and environments, rules are used to determine the optimal production organization mode and accurate constraints to construct a scheduling model, and then appropriate algorithms are employed to solve the model. The "operational rules" are based on actual production conditions and the experience of on-site schedulers, establishing fundamental principles, mainly including basic production scheduling rules, time control rules, equipment matching rules, and manual adjustment rules, forming a production scheduling rule library. The "intelligent algorithms" are computational methods for solving scheduling problems in the steelmaking-continuous casting production process, such as using heuristic algorithms and genetic algorithms to solve production scheduling plans and address static scheduling problems.

[0039] Steelmaking-continuous casting production scheduling, under the premise of a plan issued by the enterprise management department (generally based on the casting schedule as input), considers constraints such as raw material arrival, machine capacity, and process specifications. It rationally arranges the processing sequence and equipment of production tasks in each process, determines the timetable for each process operation, and aims to optimize one or more objectives such as waiting time, on-time casting start, and maximum completion time, providing decision support for the efficient, stable, and safe operation of the enterprise's production system. Due to the characteristics of steelmaking-continuous casting production operations, such as long process flows, complex processes, intersecting paths, numerous equipment, and frequent unexpected events, production operation plans obtained from manual experience or standard models often have unsatisfactory execution effects and efficiency in actual production. To further improve the efficiency of production operation plan preparation and its executability in a real-world environment, this project will develop a furnace scheduling algorithm with rapid optimization scheduling and dynamic re-optimization functions based on the Plant Simulation simulation platform. This will meet the actual needs of steelmaking production scheduling in the context of intelligent manufacturing and improve the usability of the model and algorithm. The simulation-optimization interface scheme of this furnace scheduling algorithm is as follows: Figure 3 As shown.

[0040] 4) Steel Ladle Scheduling Optimization Algorithm A ladle is a container for holding and transporting molten steel for secondary metallurgical processes. It serves as an auxiliary transfer device in the steelmaking process, closely related to the stable control of molten steel temperature, the timing of processes within the steel plant, and logistics control. Optimizing ladle turnover can improve the hot turnover rate of ladles and reduce the cooling time of empty ladles. The ladle selection problem involves determining the allocation and scheduling of ladles that meet a series of process constraints based on production scheduling. Therefore, the optimal ladle scheduling algorithm must satisfy equipment assignment within the heat plan. Based on the heat plan, it selects ladles to receive the heat, determines the overhead cranes used for transporting the ladles, and the crane transport routes, ensuring that the heat arrives at the assigned equipment within the specified time according to the production schedule and starts operation on time.

[0041] In steelmaking, molten iron received from the blast furnace is transported to the steelmaking workshop. If the double-running process is used, the molten iron is added to the dephosphorization converter, and the molten iron ladle completes its turnover. After the dephosphorization converter smelting is completed, a semi-ladle is used to receive the molten steel from the dephosphorization converter. The semi-ladle then transfers the dephosphorization converter molten steel to the decarburization converter, where it is received by the normal ladle. The refining and continuous casting processes are then completed. (See process details below.) Figure 1 The shaded area in the image represents the offline cold repair process, which is not considered in the model.

[0042] If a conventional decarburization converter is used for steelmaking, the molten iron is added to the converter, and the ladle completes its turnover. After the converter smelting is completed, the ladle receives the molten steel from the carbon converter, and then completes the refining and continuous casting process. The ladle turnover process is detailed below. Figure 4 The ladle turnover process is as follows: Figure 5 The shaded area in the image represents the offline cold repair process, which is not considered in the model.

[0043] As can be seen from the above analysis, there are three types of ladles used for the main logistics in steel plants: molten iron ladles, semi-molten steel ladles, and steel ladles. Therefore, the model needs to consider the turnover of these three types of ladles. The turnover of molten iron / steel ladles is divided into two parts: online and offline (shaded in the figure). Since the offline part is mainly for various maintenance and baking standby processes, it is not considered in the model algorithm; the model only considers the scheduling of online turnover ladles.

[0044] Based on the above analysis, the main flow of molten iron ladles is from blast furnace tapping to the dephosphorization converter process. The ladle receives molten iron at the blast furnace feeding station. After tapping, the ladle is transported by trolley and overhead crane to the pretreatment station. After pretreatment, the ladle, along with scrap iron and alloy materials, is added to the dephosphorization converter by overhead crane. The empty ladle, after adding the molten iron, is transported by overhead crane to the receiving station to await blast furnace tapping. The transportation of molten iron ladles is mainly divided into empty ladle transportation and full ladle transportation. Empty ladle transportation refers to the state where the ladle, after receiving molten iron at the feeding station, is empty after adding the molten iron to the converter; full ladle transportation refers to the state where the ladle contains a large amount of molten iron after receiving it at the feeding station. The full ladle transportation process is from receiving molten iron at the feeding station to the end of adding the molten iron. A detailed schematic diagram of the molten iron ladle flow process can be found here. Figure 6 .

[0045] The flow of semi-steel ladles mainly involves the transition from the dephosphorization converter process to the steelmaking process. The semi-steel ladle receives molten iron in the dephosphorization converter, and the molten steel from the dephosphorization converter is transported to the decarburization converter via overhead cranes and trolleys. After the molten iron is added, the empty ladle is transported by overhead crane to the waiting position in the dephosphorization converter. A detailed schematic diagram of the semi-steel ladle flow process can be found here. Figure 7 .

[0046] The ladle's flow primarily involves the transition from the decarburization converter process to the continuous casting process. After converter smelting, the empty ladle receives molten steel and, depending on the steel grade, is transported by overhead crane and trolley to the refining equipment for refining. The refining process mainly includes ladle refining and vacuum circulating degassing furnace refining. After refining, the trolley moves out of the refining equipment, and the overhead crane lifts the ladle to the rotary table in front of the continuous casting machine, pouring the molten steel into the tundish. The molten steel in the tundish rapidly solidifies and crystallizes in the crystallizer to form slabs. Because there is slag at the bottom of the ladle, it needs to be removed. Therefore, the overhead crane transports the completed ladle to the slag removal station for slag removal. The ladle requiring repair will undergo a series of modification operations at the next turntable station. Afterward, the ladle will have the following destinations: (1) transported to the converter in the steelmaking section to receive a new batch of molten steel, directly entering the next transfer cycle; (2) returned to the ladle preheating and baking area for baking, awaiting the next batch allocation; (3) transferred to the empty ladle area to cool naturally. During the ladle transportation process, the transport from the decarburization converter to the continuous casting process is a loaded ladle transport, while the remaining transport processes are empty ladle transport. For a detailed schematic diagram of the ladle circulation process, please refer to [link to schematic diagram]. Figure 8 As shown.

[0047] 4) Simulation model assumptions (1) Simulation modeling: All information such as the start (stop) time of each planned casting, the number of continuous casting furnaces, the continuous casting machine and the steel grade are known in advance. This information can come from the data analysis layer.

[0048] (2) The steel casting process follows the principle of constant casting speed. The casting cycle of the continuous casting machine in the simulation model is determined according to the cross-sectional specifications of the continuous casting billet.

[0049] (3) During the operation of the empty ladle, all processes are strictly operated in accordance with the regulations, and the operation cycle is set to a standard constant value.

[0050] (4) The ladle trolley is mainly used as a buffer station for ladle transportation, and its impact on the operation of multiple processes is ignored.

[0051] (5) The simulation model does not consider the case of the ladle being taken off the production line after major overhaul.

[0052] 5) Application of simulation models For the complex steelmaking-continuous casting production process, determining a reasonable connection mode between the converter, refining furnace, and continuous casting machine is of great significance for optimizing production operation organization and overhead crane scheduling. The simulation model can conduct full-process simulation exercises for different production modes and organization methods in the workshop layout, aiming to find a more rational and simple workshop layout, more reasonable and effective process buffering, and more efficient and optimized process operation, while analyzing the bottlenecks in the steelmaking-continuous casting workshop.

[0053] Analysis and Optimization Layer: This is the core functional module of the virtual-real system collaborative optimization method, responsible for in-depth analysis and optimization decisions based on the multi-scheme, multi-scenario operational results output by the simulation modeling layer. Based on logistics behavior data, equipment status data, and time parameter data output by the simulation platform, this layer uses a built-in analysis and optimization evaluation system algorithm to achieve dynamic adjustment and multi-objective optimization of the production plan. Specific functions include: 1) Multi-objective optimization analysis The core optimization objectives are to maximize output, maximize equipment utilization, and maximize ladle turnover. Establish a multi-objective optimization evaluation model that comprehensively considers factors such as process matching degree, overhead crane conflict rate, and temperature drop control. 2) Bottleneck identification and mitigation strategies Based on simulation results, bottleneck processes in the production flow (such as refining waiting, crane conflict, continuous casting interruption, etc.) are identified, and suggestions for bottleneck mitigation are provided, such as adjusting equipment allocation, optimizing crane path, and adding buffer stations. It supports comparative analysis of different solutions and simulates system performance changes under different mitigation strategies; 3) Calculation of laminar flow operation index: Calculate the laminar flow operation index to assess the level of continuous operation in continuous casting; Quantify the system's operational stability by combining the furnace machine's corresponding mode and the matching degree of the process rhythm; 4) Optimization of ladle and slag pot turnover Based on the statistics of steel ladle and slag pot turnover time, the "flexible time" in the turnover process is identified; Optimize the path allocation and baking strategy for ladles and slag pots to reduce empty ladle waiting time; 5) Analysis and resolution of overhead crane scheduling conflicts Statistical analysis of the number, duration, and area of ​​overhead crane conflicts; The crane task sequence is dynamically adjusted based on a rule base (such as avoidance rules and task priority rules). Provides conflict early warning and real-time rescheduling suggestions.

[0054] 6) Co-optimization of molten steel temperature and time Establish a predictive model for the change of molten steel temperature over time; Temperature drop constraints are embedded in production scheduling to optimize the time nodes of each process and ensure that the pouring temperature meets the standard. It supports the optimization of temperature control strategies for different steel grades and different process paths.

[0055] Control Application Layer: This is the interface layer for system-user interaction and the bridge between optimization results and implementation. This layer transforms the output of the analysis and optimization layer into actionable production instructions and a visual monitoring interface, enabling real-time control and dynamic adjustment of the production process. Specific functions include: 1) Visual monitoring dashboard It provides a 3D virtual workshop tour function, which displays the real-time operating status of overhead cranes, ladles, and process equipment; It integrates a real-time data dashboard to display key indicators such as temperature, time, equipment utilization, and conflict rate; It supports multi-screen split-screen display, simultaneously monitoring multi-dimensional information such as logistics, temperature, and scheduling.

[0056] 2) Issuance of dynamic scheduling instructions The optimized production plan (furnace plan, casting plan, ladle allocation plan, etc.) is sent to the MES, PLC and other control systems through the data interface; It supports both manual confirmation and automatic execution modes to ensure the reliability and flexibility of commands.

[0057] 3) Real-time data feedback and closed-loop optimization Receive real-time data from the perception layer and compare it with the simulation prediction results; When deviations occur (such as abnormal temperature, crane delay, or equipment failure), the re-optimization process is automatically triggered. Achieving a closed-loop operation mechanism of "perception-simulation-optimization-control".

[0058] 4) Report generation and decision support Automatically generate daily production reports, efficiency analysis reports, optimization suggestion reports, etc. It supports historical data grayscale and comparative analysis, providing data support for management decisions.

[0059] Provides data interfaces to support integration with enterprise management systems such as ERP and QMS.

[0060] By creating a collaborative optimization architecture and analysis and evaluation method for the steelmaking-continuous casting production organization virtual-physical system, a virtual-physical integrated logistics simulation platform is constructed. This platform integrates actual production data with simulation models, enabling real-time tracking and optimization of production data throughout the entire lifecycle of the steelmaking-continuous casting process. This achieves multi-scale collaborative optimization from process to scheduling, improving overall efficiency. The trial-and-error process is moved from physical to virtual, significantly reducing experimental costs and risks. Simulation verification and prediction of future states shorten production R&D and debugging cycles. Real-time perception and automatic adjustment of process deviations improve quality consistency and flexibility. Optimization of ladle turnover and crane scheduling significantly reduces energy consumption and operating costs. System laminar flow operation and multi-process matching analysis improve the continuous operation rate of continuous casting. Three-dimensional visualization simulation of the steelmaking-continuous casting workshop provides visual decision support, enhancing the transparency and intelligence of production management.

[0061] Example: This invention provides a collaborative optimization analysis method for a virtual-physical system in steelmaking continuous casting production organization, belonging to the field of intelligent manufacturing technology in steel production. By constructing a data acquisition layer, data analysis layer, simulation modeling layer, analysis optimization layer, and control application layer, and combining "operation rules + intelligent algorithms" to collaboratively optimize production planning and scheduling, it improves equipment utilization, ladle turnover rate, and continuous casting laminar flow operation level. Simultaneously, it enables real-time prediction, dynamic scheduling, and visualized control of the production process, significantly improving the intelligence level and operational efficiency of steelmaking production organization.

[0062] A steel plant currently has four 210t converters, three double-station LF furnaces, three double-processing-station RH vacuum refining furnaces, and five continuous casting machines. Given the existing plant area and process facility layout, it is necessary to optimize the workshop process facility layout, add new process facilities, and optimize the workshop production organization mode to achieve an annual steel processing capacity of 13.5 million tons while ensuring smooth material flow. Therefore, to increase the demand for converter and continuous casting capacity under the existing workshop production capacity, it is considered to add one converter, one LF furnace, and one continuous casting machine to meet the increased capacity demand. Based on this requirement, and combined with the system architecture and analysis method provided by this invention, a computer server is built at the data analysis layer to collect sample data in real time for the entire process control of steelmaking-continuous casting, including process time, molten steel temperature, steel grade production mode, crane equipment parameters, ladle and slag pot operation parameters, etc., which are required for the entire material flow control analysis. Combined with the data statistics / analysis layer, the average operating time (in minutes) of each process / equipment (converter, double-station LF furnace, double-processing-station RH vacuum refining furnace, continuous casting machine, and hot repair is obtained, as follows: 1) The converter operation cycle table is shown in Table 1.

[0063] Table 1

[0064] 2) The LF furnace operation cycle table is shown in Table 2.

[0065] Table 2

[0066] 3) The average operating cycle of the RH furnace is shown in Table 3.

[0067] Table 3

[0068] 4) Average work cycle table for hot repair of steel ladles The steelmaking-continuous casting workshop is equipped with two span-type hot repair lines and two horizontal ladle systems. The molten steel ladles are rotated once a week for hot repair, with an average hot repair time of 22 minutes. 5) Average operating cycle table for cross-pass vehicles To facilitate the hot repair of empty ladle in the steel receiving bay to the refining bay, one hot repair vehicle is installed on the left side of each of 1#RH and 1#LF, and one hot repair vehicle is installed on the right side of 2#LF and the left side of 4#CC. The average operating speed of the hot repair vehicles is 30 m / min.

[0069] 6) The average operation cycle of continuous casting is shown in Table 4.

[0070] Table 4

[0071] 7) The average operating parameters of the crane are shown in Table 5.

[0072] Table 5

[0073] Specifically, a multi-process operation simulation model is built on the Plant Simulation logistics simulation software platform, and a simulation optimization planning and scheduling platform is integrated. Through the collaborative optimization of production planning and scheduling by "operation rules + intelligent algorithms", accurate simulation and prediction of actual production operations of multiple processes / equipment can be achieved.

[0074] Specifically, based on the workshop layout of the steelmaking-continuous casting section, the process layout diagram is simply processed by removing overhead cranes and ground vehicles from the drawing, and then the workshop process layout diagram is imported into the Plant Simulation simulation platform.

[0075] Specifically, drag the required class modules for the implementation case into the Frame model framework within the Plant Simulation simulation platform. These mainly include LD, LF, RH, CC, Crane, and auxiliary class modules. The implementation case's 3D model block diagram is shown below. Figure 9 As shown.

[0076] Specifically, the average job time parameter transmitted from the data analysis layer is input into various modules.

[0077] Specifically, data such as the location of each equipment hoisting point, steel grade process path, casting schedule, and furnace schedule information are input into the simulation optimization planning and scheduling model software. The production scheduling model based on "operation rules + intelligent algorithms" automatically generates casting schedule tables, furnace schedule tables, and crane scheduling tables, which are then sent to the Plant Simulation simulation platform.

[0078] Specifically, the simulation model is started on the Plant Simulation platform, and simulation data is generated through simulation operation, including but not limited to single process waiting time, continuous casting laminar flow level index, Gantt chart and other data information. This simulation data is then returned to the simulation optimization planning and scheduling model through the simulation platform and the simulation optimization planning and scheduling model via the data interface to dynamically adjust the continuous casting machine pouring start time and the crane scheduling plan. Through iterative optimization algorithm, the new pouring plan table, furnace plan table and crane scheduling table are then sent to the Plant Simulation platform.

[0079] Specifically, the simulation model is restarted in the Plant Simulation simulation platform, and so on, iterating the casting schedule, furnace schedule and overhead crane scheduling until the logistics of the steelmaking-continuous casting workshop are smooth and the continuous casting machine can continuously cast furnaces, only then is the model calculation completed.

[0080] Specifically, through simulation model calculations of different process paths for several steel grades, the simulation data such as the production ratio of different steel grade paths, the crane operation rate, operating area, and conflict rate of the feeding span and molten steel receiving span, and the workshop production Gantt chart are output. These data will be output to the analysis and optimization layer.

[0081] Specifically, the data analysis and optimization layer uses a built-in analysis and optimization evaluation system algorithm to achieve dynamic adjustment and multi-objective optimization of the production plan.

[0082] Specifically, after data analysis, a simulation report is generated: 1) Under the current workshop process layout and continuous casting machine pouring operation parameters, it can complete the production of 185 heats of molten steel per day.

[0083] 2) In terms of crane operating rate, the operating rate of each crane in the steel receiving span (5 380 / 80t casting cranes) and the refining span (4 160 / 50t cranes) is less than 85%, and their capacity and number configuration can meet the production scheduling requirements of 185 heats of molten steel per day.

[0084] 3) Due to the limitations of different steel refining paths, the proportion of molten steel through the LF+RH refining path reaches a maximum of 44.86%; the proportion of molten steel through the RH refining path reaches a maximum of 21.08%; and the proportion of molten steel through the LF refining path reaches a maximum of 30.81%.

[0085] 4) Under the premise of a steel output of 230 tons, an effective number of working days per year of 325 days, and a continuous casting machine yield of 0.98, an annual output of 13.5 million tons can be achieved.

[0086] 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 method for collaborative optimization analysis of the virtual and real systems in steelmaking continuous casting production organization, characterized in that: Includes the following steps: S1: Collect material flow sample data of the entire steelmaking-continuous casting process through the data acquisition layer; S2: Classify and analyze the material flow sample data through the data analysis layer to establish a process analysis data analysis module; S3: Through the simulation modeling layer, a virtual simulation model of the steelmaking-continuous casting process, a virtual turnover model of the ladle / slag pot, and a simulation optimization planning and scheduling model are constructed, and a virtual-real integrated logistics simulation platform is built. S4: Perform multi-objective optimization analysis and bottleneck identification on the simulation results output by the virtual simulation model through the analysis and optimization layer; S5: Enables visualized monitoring of production data, dynamic scheduling command issuance, and closed-loop optimization through the control application layer.

2. The method for collaborative optimization analysis of the virtual and real systems in steelmaking continuous casting production organization according to claim 1, characterized in that: In S1, the material flow sample data includes at least one of the following: process time parameters, molten steel temperature parameters, steel grade production mode, crane equipment parameters, ladle operation time parameters, and slag pot operation time parameters.

3. The method for collaborative optimization analysis of the virtual and real systems in steelmaking continuous casting production organization according to claim 1, characterized in that: In S2, the process analysis data parsing module includes at least one of the following: process time parameter parsing module, molten steel temperature parameter parsing module, steel grade production mode parsing module, crane equipment parameter parsing module, ladle operation parameter parsing module, and slag pot operation parameter parsing module.

4. The method for collaborative optimization analysis of the virtual and real systems in steelmaking continuous casting production organization according to claim 1, characterized in that: In S3, the simulation modeling layer uses a logistics simulation software platform to construct a multi-process operation simulation model and integrates a simulation optimization planning and scheduling platform to collaboratively optimize production planning and scheduling through "operation rules + intelligent algorithms".

5. The method for collaborative optimization analysis of the virtual and real systems in steelmaking continuous casting production organization according to claim 4, characterized in that: The logistics simulation software platform is the Plant Simulation software platform, which contains various types of module sets for custom processes / devices. These module sets include at least one of the following: crane module set, dimension control module set, ladle refining furnace module set, converter module set, continuous casting machine module set, vacuum circulating degassing refining furnace module set, desulfurization module set, vacuum degassing furnace module set, electric arc furnace module set, molten iron pretreatment station module set, and blast furnace module set.

6. The method for collaborative optimization analysis of the virtual and real systems in steelmaking continuous casting production organization according to claim 4, characterized in that: The "operation rules" include basic production scheduling rules, time control rules, equipment matching rules, and manual adjustment rules; the "intelligent algorithms" include genetic algorithms, heuristic algorithms, or dynamic programming algorithms, used to optimize output, equipment utilization, and ladle turnover.

7. The method for collaborative optimization analysis of the virtual and real systems in steelmaking continuous casting production organization according to claim 1, characterized in that: S4 specifically includes: performing multi-objective optimization analysis on the simulation results, identifying bottleneck processes, calculating laminar flow operation index, optimizing ladle and slag pot turnover, analyzing and resolving crane scheduling conflicts, and optimizing molten steel temperature-time coordination.

8. The method for collaborative optimization analysis of the virtual and real systems in steelmaking continuous casting production organization according to claim 7, characterized in that: The laminar flow operation index is used to quantitatively evaluate the continuous operation level of the continuous casting machine.

9. A collaborative optimization analysis system for the virtual and real systems of steelmaking continuous casting production organization for implementing the method described in any one of claims 1 to 8, characterized in that: include: The data acquisition layer and the data analysis layer together constitute the statistical data analysis system for the steelmaking-continuous casting process; The simulation modeling layer and the analysis and optimization layer together constitute a steelmaking-continuous casting virtual simulation system. The control application layer constitutes a visual display and control system.

10. 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 8.

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