| Cite this article as: |
Te Xu, Guang Song, Yang Yang, Pei-xin Ge, and Li-xin Tang, Visualization and simulation of steel metallurgy processes, Int. J. Miner. Metall. Mater., 28(2021), No. 8, pp.1387-1396. https://dx.doi.org/10.1007/s12613-021-2283-5
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Steel production involves the transfer and transformation of material and energy at different levels, structures, and scales, and this process incurs substantial information in the material and energy dimensions. Given the black-box feature of iron and steel production processes, process visualization plays an important role and inevitably benefits parameter correction, technical support decision-making, personnel training, and other aspects of the steel metallurgy industry. The technological characteristics of the entire process in the steel industry were analyzed in this study, a visualization technology route based on virtual reality (VR) was built, and the important components of visual simulation system for steel industry and the important technical points needed to realize the system were proposed. On the foundation, a visual simulation model for the process scheduling of the iron and steel enterprise raw materials’ field, slab, and hot rolling processes was built, and a visualization simulation platform of the iron and steel metallurgy plant-wide process, including ironmaking, steelmaking, hot rolling, and cold rolling, was developed. Lastly, the effectiveness of platform was illustrated by practical application.
China is a major producer and consumer of steel. However, compared with that of developed countries, China’s manufacturing industry is still in the extensive production management mode. In the face of new environments and new production demands of China’s steel industry, the reform of enterprise management mode must be promoted urgently through technological innovation and method innovation; moreover, the intelligence level of manufacturing processes must be improved, energy must be saved, emissions must be reduced, and green manufacturing must be realized [1]. Smart steel mills coordinate the information methods and manufacturing process on the basis of a comprehensive, integrated process of the material, energy, and information to realize information self-perception, smart self-decision, exact control, and self-adaption [2]. On the one hand, the intellectualization of iron and steel production depends on the intelligent decision-making of the production process brought about by data analysis and optimization [3−4]; on the other hand, it depends on more cutting-edge technological means to realize intelligent man–machine interaction, pattern recognition, and other functions. Visual simulation based on virtual reality (VR), augmented reality (AR), and other technologies is a new direction of application in the industrial field in recent years. Given its intuitive feedback effect, intelligent methods of man–machine interaction have been adopted by an increasing number of industrial applications.
VR technology is a new visualization and human–computer interaction technology that integrates computer graphics, multimedia technology, human–computer interaction technology, network technology, stereo display technology, and simulation technology. Research on visualization based on VR is a frontier topic in the field of artificial intelligence and intelligent human–computer interaction in recent years. These visualization technologies have gradually expanded from the game, teaching [5−8], commercial [9], medical [10], and other fields to industrial applications [11−12]. Many large iron and steel manufacturing enterprises have begun to use simulation and visualization technologies to improve production and training efficiency [13−15]. Meanwhile, engineers also strive to integrate simulation and VR into visual engineering applications [16−17]. Burdea and Coiffet [18] summarized the important characteristics of VR: immersion, interaction, and imagination. National Aeronautics and Space Administration (NASA) has established the aerospace aviation and satellite maintenance VR training system and the space station VR training system. NASA has also set up the VR education system. Ala-Siuru et al. [19] from the Finnish Technical Research Center introduced a visual environment system for equipment fault diagnosis in the Lautaruji Steel Plant in Finland. VR is most effective in the field of production and assembly, where integrated production and process development work can be performed in a visual three-dimensional (3D) environment. VR technology can also be used to strengthen several manufacturing applications, including analysis, logistics, and ground layout (production equipment).
Steel University was founded on the initiative of the WorldSteel Association. It adopts the visual interactive electronic resources related to advanced iron and steel technology, which covers steel production, products, application, and recovery, and provides multiple simulation platforms, such as blast furnace ironmaking, visual steel mill, converter steelmaking, electric steelmaking, secondary refining, and continuous casting [20]. The Center for Innovation through Visualization and Simulation of Purdue University considers that VR can provide cost-effective tools for process optimization, design, troubleshooting, virtual design, and virtual training and provide a solution for energy, environmental, productivity, safety, and quality problems in steel and other industries [21]. Lastra et al. [22] summarized software tools for multiagent system simulation and visualization in current industrial applications and proposed a set of agent-oriented 3D creation, simulation, and visualization software. Moreland et al. [23] also proposed a way to create interactive security training software. This approach uses VR to create 3D models and interactive scenes with a realistic experience. Users can interact with the model in real-time through script scenarios and understand and apply security concepts to complete relevant tasks. There are still gaps in VR research between China and some developed countries. Hao et al. [24] studied a data presentation method in the visual management system of steel enterprises. They analyzed various data resources to realize information integration, studied the communication principle between flash animation and database, and designed a reasonable animation database structure to maximize the universality of the program code. Zhang and Song [25] proposed a simulation and decision system based on operation optimization, aiming at the failure of manual decision-making to obtain a stable and satisfactory value of the coke ratio of radial ore and the failure to make timely and accurate adjustments when the blast furnace condition changes. After the actual operation of a certain blast furnace, the system improves the stability of the furnace condition, which meets the requirements of technical indexes, such as the scientific and stable control of the radial coke ratio in the industrial site, and provides a timely adjustment plan when the blast furnace condition changes.
The above researches based on VR of iron and steel metallurgy industry visualization simulation learning environment are mainly concentrated on two aspects. (1) The first is a 3D reconstruction of the iron and steel metallurgy process, which produces a detailed observation of the production process by means of 3D simulation and allows beginners to have a more intuitive understanding of the entire process of iron and steel production. (2) Combined with a mathematical simulation model, the typical processes or changes in iron and steel metallurgical production are displayed dynamically through a 3D model. In conclusion, the application of VR in the iron and steel industry provides a new solution to traditional problems in iron and steel enterprises. However, the development and application of VR are still in the exploratory stage, and application experiences in the field of the iron and steel industry are not enough. The fusion of VR and the iron and steel industry still faces a series of problems. With the continuous development of VR technology, it will play an important role in parameter correction, technical support to decision-making, personnel training, and process visualization in the steel industry.
The simulation model based on VR, AR, and visual simulation technology can accept the preprocessed actual production data, simulate the on-site production in a relatively safe environment in real-time, and convert some black-box problems into white-box problems. By contrast, the simulation model can be visual production or manufacturing in the terminal equipment to correct the mathematical model based on data analysis and optimization and form closed-loop control and monitoring.
Fig. 1 shows the frame of the visualization application research for the entire process of the iron and steel metallurgy industry, whose final products are coils or sheets. On the basis of the data acquisition from the Cyber Physics System or even the Human Cyber Physics System, the important technical problems of industrial processes are analyzed and optimized through data analysis and optimization technology and the combination of computational fluid dynamics and actual production. The VR and AR technologies are used to build the visual model and visual simulation model, which are used to implement scheduling optimization, visual production, real-time monitoring, and other functions, and eventually establish an intelligent visualization platform for the entire process for the steel metallurgical industry. The visual and visual simulation models are two important parts of visual simulation platforms. The former focuses on the realization of three-dimensional perception, whereas the latter focuses on the simulation of actual production.
In the visual modeling stage, the most important direction is to restore the 3D display effect of the production environment to the greatest extent; therefore, typical hardware and software platforms, such as high-configuration graphic workstations, rendering server clusters, storage server clusters, Unity3D engine, and 3DMax system, are adopted, and mainstream VR hardware are equipped to complete the construction of the entire system’s research and development environment. On the basis of the Unity3D engine, a basic platform for theoretical research is built and run in the above environment to test the performance and effect of relevant algorithms. Under the Windows system, C# language is used to complete the development of core systems, such as the analysis, communication, rendering, simulation, and visualization systems, on Unity3D and Visual Studio. Key technologies and demonstration applications are verified in detail through a series of metallurgical industry scenes and processes, such as raw material farms and slab warehouses.
3D visualization technology represents real-world objects in the virtual world vis 3D technology based on computer technology [21]. Thus, it has a huge improvement in terms of complex spatial information expression of objects compared with two dimensional (2D) images. Meanwhile, it also has real-time interaction ability. The visualization process can be broadly divided into modeling, rendering, animation, programming, and generation. (1) Modeling: modeling of objects in the real world on the basis of the outline or wrapping surface; (2) Rendering: sensory simulation of the established model to reproduce the impression of the brain on the real object, mainly through materials, transparencies, maps, and special effects; (3) Animation: animation of the static model; (4) Programming: flexible invocation of a static model or an animated demo, including selective action trigger condition setting, angle selection and follow-up, and object parameter setting; (5) Generation: generation of the complete scene.
Compared with the 2D static picture, visual simulation technology has greater advantages in information perfection and consistency, and simulation is more intuitive, clear, and real in information transmission. In addition, through the development of the visual model, a visual model that can be embedded in the algorithm is formed, the feasibility of the algorithm is tested in the simulation, and the feedback data and other research bases are provided to facilitate the algorithm modification. Therefore, the research and development of the interactive 3D dynamic view, the physical behavior of the slab warehouse logistics visual simulation system, the dynamic simulation of the actual logistics process scene, and the expected scheme simulation test have important practical application value.
VR-based visual simulation technology can be divided into offline and online simulations according to data transmission mode. The basic logic architectures of the two methods are similar, but online simulation requires high requirements for real-time data processing and transmission. In industrial sites, especially in the complex industrial sites of iron and steel metallurgy, production data need to be scientifically processed before they can be used in a visual simulation model. Therefore, visual simulation platforms of the iron and steel metallurgy industry generally include the following important steps, as shown in Fig. 2.
(1) Fluid dynamics or discrete event simulation engines are designed. The engines mainly realize the basic logic level fluid dynamics or discrete event simulation function. For example, the discrete event simulation engine can process each work content in complex production environments in serial or parallel by setting up the event occurrence list.
(2) A 3D visual simulation model is built. On the basis of the simulation engine, the visual simulation model that conforms to the actual production situation is established. The model should include all relevant resources, equipment, and personnel that affect the production status. The data interfaces are established for the key device models, and the basic simulation logic program is nested.
(3) Production data are preprocessed. The actual production data from the field are converted into data that can be identified and used by the visual simulation model. For example, a multifield coupling mathematical model of key equipment is established for steelmaking process production, and relevant information, such as heat transfer, mass transfer, bubble flow, slag layer movement, and solidification phase transition, are calculated. The probability distribution statistics of the production equipment’s production time, input and output information, and fault time are conducted for the process that is close to the discrete manufacturing production mode (e.g., cold rolling).
(4) The preprocessed production data are imported into the visual simulation model to simulate the actual production situation for visual production. The veracity of the visual simulation model is verified by simulation results. On this basis, the visual simulation model is improved and optimized until the simulation conditions are satisfied.
Through the above steps, a visual simulation platform, which is highly coincident with the real world, can be obtained, and various experiments can be performed on this system. By controlling the running clock in the visual simulation system, we can simulate the actual production situation of the real world for one year or more in a short time (e.g., one hour). By using the probability distribution of historical, the system can simulate the occurrence of random events and catch important events that may occur but have not occurred in the real world. At the same time, by changing the relevant important parameters of the visual simulation system, such as the production time of equipment and the probability of failure, the changes of production efficiency, production quality, and production failure when the production system changes can be known.
In this study, the VR-based visual simulation practice can be studied from the following directions: research on the visual method of discrete event simulation based on the fusion of queuing scheduling and data analysis and establishment of a visual simulation experiment and learning platform based on data analysis and optimization. On the one hand, the 3D reproduction of industrial processes is realized and thus can present the production process that cannot be observed in detail on the production site’s 3D simulation. On the other hand, in combination with the mathematical simulation model, the typical processes or changes in iron and steel metallurgical production can be simulated, and the possible troubles are displayed dynamically through the 3D model. In general, the decision is usually sent to physical systems to be executed directly. As indicated in Fig. 3, the initial plan made by algorithms or planners can be sent to the developed visual simulation platform, which is developed on the basis of the physical systems, to test its performance. If some possible troubles can be found through simulation, then the plan is improved. The plan can be sent to the execution systems until it is passed by the visual simulation platform without problems. Thus, the establishment of a visual simulation platform can become a test platform for making the production more stable, thereby maximizing the concepts of intelligence and digitization to achieve new breakthroughs in China’s industrial software.
The raw material field of iron and steel enterprises is a large logistics scheduling site for receiving, storing, processing, and blending raw iron and steel metallurgical raw. Many modern and large raw material fields of iron and steel enterprises include ore, coal, auxiliary raw material, and mixing fields, which store the purchased iron ore, iron concentrate, coking coal, and thermal coal, as well as some sinter, pellet, and circulating materials. Large equipment commonly used in the raw material field includes stackers, reclaimers, stackers, and belt conveyors. The raw material fields of many steel enterprises are close to the wharf, which is convenient for foreign transport ships to dock and unload raw materials; thus, the wharf also includes large-scale dispatching equipment, such as shore cranes. The layout of raw material fields usually includes strip and circular material fields, and many differences can be found in the storage modes corresponding to each material field. On strip fields, stacker–reclaimers take the operation in each of the material strips. The stacker–reclaimer’s arm is long and has safe working distance limits; thus, under limited conditions, operation conflicts in stacker–reclaimers may occur, thus greatly reducing the work efficiency of the stacker and causing hidden trouble to downstream production.
In this study, a 3D simulation model of a raw material field of an iron and steel enterprise is established on the basis of the visual simulation technology of VR. The model includes important production scenes and equipment, such as strips, belt machines, and stacker–reclaimers, to realize the function of panoramic scheduling supervision and management of raw material fields. The 3D simulation model is shown in Fig. 4.
In the logistics management of slabs in iron and steel enterprises, cranes are often used to stack and take the slab. Cranes are an important carrier in iron and steel enterprises. In addition to the high equipment cost, the operating cost of cranes is high. If the crane cannot be properly scheduled, then it leads to instability of hot rolling production, an increase in logistics costs, and other problems. Therefore, in the logistics scheduling of a slab warehouse, the placement of slabs and the scheduling and management of cranes are important issues [26]. Given many types of slabs stacked in the slab warehouse, the stacking principle is set for the placement of the slabs. To facilitate the ex-warehouse operation and classification management of the slabs, the logistics management system of the slab warehouse provides the target stack position, and the train moves the slab in accordance with the target stack information before the slab is put into storage. The slabs in the warehouse have different specifications; thus, the sources, transport means, and ex-warehouse destination of the slabs differ. Therefore, the area of the slab warehouse must be divided in accordance with the basic principle of making it convenient for slab storage and not affecting hot rolling production. In general, the slabs in the slab warehouse have strong mobility, and there must be numerous frequent logistics operations in the warehouse. In actual production, the slab warehouse mainly completes these logistics operations through vehicles, roller beds, and other tools [27]. In this study, a 3D simulation model of slab logistics scheduling management in an iron and steel enterprise is established through the visual simulation technology of VR, including important production scenes and equipment, such as cranes, to realize the panoramic supervision and management function of slab logistics management. The 3D simulation model is shown in Fig. 5.
Hot rolling manufacturing systems include three main processes: slab warehouse, hot rolling production, and coil warehouse. According to the requirements of the rolling process, hot rolling manufacturing needs to combine slabs into a basic rolling unit according to the properties of width, thickness, hardness, and temperature, which is a typical batch of hot rolling process. Cold rolling takes a hot rolling product as raw material. The production process generally includes raw material preparation, pickling, rolling, degreasing, annealing (heat treatment), and finishing. The product is the steel plate that is further rolled thinly to the target thickness under the recrystallization temperature. Compared with hot-rolled steel plates, cold-rolled steel plates have a more accurate thickness, and the surface is smoother and more beautiful. Given that cold-rolled steel is obtained from hot-rolled steel after the cold rolling process and that cold-rolled steel also has some surface finishing, cold-rolled steel has better surface quality (such as surface roughness) than hot-rolled steel. Research on traditional hot rolling process production scheduling mainly combines a mathematical model and an optimization algorithm and is usually only for the algorithm design and simulation of the hot rolling process itself and thus rarely involves the logistics scheduling of slab and coil stock [28−31].
Although iron and steel metallurgical technology belongs to the category of process engineering, hot rolling and cold rolling production processes pay more attention to the optimization of the production sequence of slabs in different equipment, and its production characteristics are similar to those of discrete manufacturing industries. In case the mathematical model and analytical method cannot describe the actual production process, the discrete event simulation technology is more suitable for the knowledge expression and academic research on the production planning and scheduling of hot and cold rolling processes. Discrete event simulation is event-driven to gain insight into the entities or activities in the simulation system, process random events according to the actual or designed program rules, and simulate the actual production situation. This simulation focuses on the collection and statistics of actual production data, which can restore the actual production process to the maximum extent. Therefore, in the teaching and scientific research process, the actual production status of hot rolling manufacturing, as well as the interaction between hot and cold rolling production scheduling and logistics scheduling of upstream and downstream slab and coil stock, can be more comprehensively expressed. Figs. 6 and 7 show the visual simulation model of hot and cold rolling production scheduling in iron and steel enterprises.
On the basis of the VR visualization technology, a visual simulation platform for the entire process of iron and steel metallurgy enterprise, which includes ironmaking, steelmaking, hot rolling, and cold rolling, is established, as shown in Fig. 8. On the basis of the data analysis and optimization scheme of production logistics in iron and steel enterprises, a 3D simulation model of production and distribution in the blast furnace is realized by combining multidimensional linkage visualization technology. In combination with research on the real-time scheduling and monitoring technology of VR, a visual simulation model of molten iron logistics is realized.
The visual simulation model can be divided into online and offline cases. The online visual simulation can restore the actual production situation through real-time data transmission and display the actual production data that managers care about in the simulation model in real-time. On the one hand, it can macroscopically represent real-time data, including inventory, production, and logistics amounts; realize order tracking in the entire production process; and provide the analysis results of production efficiency and equipment utilization through the data analysis method embedded in the simulation model to guide the actual production. This approach is more comprehensive than the fragmented and phased production management in the usual actual production, and it can also trace the cause of the fault in accordance with the playback technology. On the other hand, with data communication and simulation technology, the actual production equipment can be controlled by the operation of the simulation model in the future. The offline visual simulation is close to the simulation optimization technology. Through the acceleration work in the computer terminal, the simulation model can simulate the production status in a month, a quarter, or even a year to preview the application of optimization results in actual scenes.
In accordance with the completion sequence of the visual simulation model described above, the input data and simulation logic of the simulation model must be studied and optimized while building the typical process visual model so that it can be embedded in the later visual simulation model and form the simulation platform. This study enumerates the input data and simulation logic of the entire process inventory management simulation of iron and steel metallurgy.
(1) Input data of the simulation model.
The selection of input data of the simulation model is important. On the one hand, all the details of the actual production must be shown. On the other hand, details that result in the selection of many useless data that have no direct effect on the production results must be avoided. Therefore, the selection of input data of the simulation model needs an advanced in-depth study on the production process, production environment, and production results of simulation objects. Usually, the relevant data of enterprises in the past six months or one year are selected for analysis and screening. In addition, many of the actual production data recorded in the production site cannot be directly applied to the simulation model. Thus, appropriate operations, such as probability statistics, unification of measurement units, and data cleaning, must be conducted to ensure that the data are real, reliable, and collectible. Taking the visual simulation model of the entire process of iron and steel metallurgy as an example, this study analyzes the correlation of production data and selects relevant data that can directly affect the production results as the input data of the model, as shown in Table 1. Given that simulation models differ in terms of objectives, the input data of simulation models have no fixed form and content and thus need to be customized in accordance with the simulation optimization logic of each simulation model.
| Process | Input data |
| Steelmaking production stage | Time of slab cutting |
| Hot rolling production stage | Time of slab entering the furnace, time of slab leaving furnace, and time of slab curling |
| Acid rolling production stage | Time of coil start processing and time of finish processing |
| Continuous annealing production stage | Coil annealing curve, production starting time, and finishing time |
| Steelmaking→hot rolling logistics stage | Each slab’s steel grade, packing method, thickness group distance, offline or not, delivery time from steelmaking warehouse, and arriving time to hot rolling warehouse |
| Hot rolling→acid rolling and acid rolling→continuous annealing logistics stage | Delivery time of coil from hot rolling (acid rolling) warehouse and arriving time to acid rolling (continuous annealing) warehouse |
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(2) Simulation logic structure of the average turnover time of materials.
As mentioned above, the selection of input data depends on the logic of simulation optimization. Based on the historical production performance, this study simulates the average turnover time of materials in the warehouse area, including the slab warehouse, hot rolling material warehouse, and acid rolling material warehouse. If the output of the simulation model matches the actual production results more than 95%, then the simulation model is considered true and reliable. Therefore, this study can optimize the process of the simulation model according to the demand and conduct a feasibility analysis according to the output results of the optimized simulation model. According to the future changes of the actual production input data, the model can also simulate the future production results (for example, according to the simulation results of simulating the future fixed cycle), obtain the average turnover time of materials with different tapping marks, grades, balling methods, thickness group spacing, and the material turnover time of all materials, and repair the statistical values based on the actual baling cycle table and hot rolling maintenance time. For any material, in accordance with its processing path to determine the required warehouse area and on the basis of the average turnover time calculation results, the in time and out time of material through the warehouse are estimated. Lastly, the material distribution and inventory of each reservoir area are estimated; by understanding the future situation and inventory of the reservoir area, the production can be effectively guided to avoid material breakage and warehouse expansion. The workflow is shown in Fig. 9.
In the above process, the most critical step is to track the inventory of each material in the simulation model. The detail steps for tracking the inventory of each material are listed as below.
Step 1: Track and record the processing start time Bi,s and completion time Ci,s of any material i (i = 1, 2, …, n) in different production processes s (s = 1, …, S) in the simulation model, where n is the total number of the candidate materials, and S is the total number of the different production processes.
Step 2: For any material i (i = 1, 2, …, n), track and record the warehouse area set Invi that material i will pass through.
Step 3: For any material i (i = 1, 2, …, n), set the time when it enters the warehouse k∈Invi as the processing completion time of the previous process, and set the time when it moves out of the warehouse k∈Invi as the processing start time of the next process in the reservoir area.
Step 4: For any adjacent warehouses, find out all the processing paths that need to pass through the two adjacent warehouses, retrieve the material information that the processing path must pass through the two adjacent warehouses from the simulation results, calculate the average turnover time of materials with different steel grades, packing methods, and thicknesses, and calculate the average turnover time of all materials between the warehouses.
Step 5: According to the consumption time of the packing method and hot rolling detection time, the statistical average time of the warehouse area is revised and updated to form a statistical table of the average turnover time of materials and the average turnover time of all materials with different steel grades, packing methods, and thickness interval between each adjacent warehouses.
Step 6: Obtain the current warehouse area and warehousing time of the material.
Step 7: Find the average turnover time of materials with the same tapping mark, brand, packing method, and thickness interval from the table of average turnover time between warehouses, as estimated in Step 5.
Step 8: According to the turnover time of the material in the current warehouse area and the estimated average turnover time, calculate the storage time and delivery time of the material passing through each warehouse area in the future production process.
Step 9: For any warehouse k (k∈Invi
Based on the above-nested program logic, such as prediction or optimization in the simulation model, the relevant data can be recorded while the simulation is conducted on the visual simulation model platform, and the required statistics, prediction, or optimization results can be obtained after the simulation. In accordance with the results, the actual production can be guided and optimized.
VR technology is characterized via simulation to create a realistic virtual environment for users through visual, auditory, touch, and other perceptual behavior to create a strong sense of immersion experience and interact with the virtual environment to cause real-time changes. The main characteristics of VR include strong immersion, multi-interaction, and rich imagination. These features enable the operator to enter an interactive 3D visual environment generated by a computer and interact and communicate with it. With the development and rapid popularization of VR technology, the entire process scenarios of the iron and steel metallurgical industry can be simulated virtually in 3D. At the same time, on the basis of real-time data analytic results, which are the response for the principle of the iron and steel smelting in the VR system, the corresponding visual effect of iron and steel smelting is shown through VR, which can reproduce the dynamic production process and working principle of the system. The specific and detailed simulation of the process flow for different smelting needs can also provide training for operators or students on process flow, system and equipment performance, equipment installation, and other aspects and thus can effectively reduce the smelting cost and actual operation risk. By establishing a visual simulation model of logistics scheduling management in iron and steel enterprises, this study realizes an intelligent interactive simulation platform that integrates scientific research, teaching, and production, thereby providing strong support for the production and safety management of enterprises.
This research was financially supported by the Major International Joint Research Project of the National Natural Science Foundation of China (No. 71520107004), the Major Program of National Natural Science Foundation of China (No. 71790614), and the 111 Project (No. B16009).
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Figures(9) / Tables(1)
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| Process | Input data |
| Steelmaking production stage | Time of slab cutting |
| Hot rolling production stage | Time of slab entering the furnace, time of slab leaving furnace, and time of slab curling |
| Acid rolling production stage | Time of coil start processing and time of finish processing |
| Continuous annealing production stage | Coil annealing curve, production starting time, and finishing time |
| Steelmaking→hot rolling logistics stage | Each slab’s steel grade, packing method, thickness group distance, offline or not, delivery time from steelmaking warehouse, and arriving time to hot rolling warehouse |
| Hot rolling→acid rolling and acid rolling→continuous annealing logistics stage | Delivery time of coil from hot rolling (acid rolling) warehouse and arriving time to acid rolling (continuous annealing) warehouse |
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