Several factors must be considered to determine the right process or route (duplex or triplex). It's crucial to recognize that processing stainless and special steels requires modern mass production, with a strong emphasis on ergonomics and resource efficiency.
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This article outlines the requirements for modern software solutions to supervise, control, and optimize metallurgical processes. While focused on AOD and VOD, it offers valuable insights for the entire process industry in general.
We introduced the term "intelligent" digital twin in this article, as the approach combines fundamental-based models with data-driven methods. By merging the strengths of both, it creates models with significantly improved properties compared to existing ones.
A significant advantage is the abundance of journals offering free access to science-based studies on the concept of digital twins. This easy, open access to relevant publications presents an excellent opportunity to explore the topic in greater detail.
Effective steelmaking modeling requires both practical knowledge and scientific fundamentals. This article outlines key disciplines, evaluates holistic models, and proposes a structured approach to model development.
We checked the results of 32 empirical equations for calculating the liquidus temperature on typical low alloyed and stainless steels. In addition, the voestalpine slide rule was used to determine the liquidus temperature and compared with the results of the formulas from the relevant literature.
Based on our software, quasi-binary Fe-C phase diagrams for steels are generated quickly and easily. Although the Fe-C diagram is crucial, additional elements and cooling rates alter phase areas, which is vital for preventing cracking during continuous casting.
It takes effort to understand data and information, it takes effort to know and derive skills from it and it takes effort to gain wisdom. When we part with linearity and recognize that the path to wisdom requires many learning loops, real added value can arise.
Automatic Tracking of Scrap Handling and Charging
Intelligent Sensors | Optimization of the Entire Raw Material Handling
Automatic and unmanned product tracking and process supervision - a goal of all production supervisors. Together with our customer, who is running Austria's most efficient steel plant producing rebar steel, we are accomplishing a comprehensive solution to automatically track the scrap handling and charging process. Our intelligent solution tracks all movements: from the moment the scrap arrives in the plant up to the charging in the electric arc furnace.
The centerpiece of this solution are our intelligent, camera-based sensors called qurve, featuring automatic image and object detection and data processing in a completely embedded solution. Our qurves are installed at different locations along the production process to fulfill the following objectives:
- Detection of number and position of scrap railway wagons
- Detection of the exact position of the two scrap charging cranes in the bay by an unprecedented camera-based positioning solution
Linking our qurves in a comprehensive software solution together with further signals from the scrap charging cranes and the scrap bucket transfer cars we achieve a complete tracking and reporting of all scrap charging operations. The results of this project help the customer in
- achieving full transparency of the scrap handling process
- optimizing the EAF process
- optimize the supply chain management
in a completely automatic solution.
Optimization of the Processes for the Production of Ferro-Alloys
Requirement Engineering | Software Solution to Monitor, Control and Optimization
The primary goal of this project is the optimization of the production of ferro-alloys with the help of a software solution that monitors, controls and optimizes the processes. For this purpose, several workshops were carried out with this Austrian customer in order to systematically record the requirements for such a system. The traceability of all activities and process steps as well as the monitoring and control of material additions, quality control and the analysis of production performance are fundamental requirements. The potential of a holistic process model was the centerpiece of this requirement engineering project to optimize and control the entire production process.