Generally, understanding something as a system means not to be limited to examine partial aspects, but to grasp the process effect in its entirety.
IN A NUTSHELL
Computational Metallurgy applications are intelligent software solutions in the field of metallurgical process modeling. These process models map the different sub-processes of steel making in a holistic approach. Computational Metallurgy applications are useful tools for optimizing the entire production process and for supporting decision-making.
The detection of inefficiencies to minimize production costs is one of the key sucess factors of a smart factory. Next to predictive maintenance, process simulations and the adaptation of process parameters play an essential role in reducing production costs. These two subjects, the process simulation and the adaptation of process parameters, are the focus of our Computational Metallurgy applications.
Generally, on-line models calculate the current state of the metallurgical process in real time on the basis of all necessary process data and in combination with a variety of model parameters. However, a verification of this calculation can only be done when comparing calculation results and measurements (such as measured temperatures and analyses of the melt and slag). If deviations are detected, model parameters have to be adapted (in the simplest case) or model improvements have to be carried out. Subsequently, these changes or adjustments must be verified again. This time-consuming process can often only be done during industrial production. The effects of the modifications are therefore only visible very late, after a sufficient amount of production data has been collected and validated thoroughly. The concept of the digital twin helps to overcome this disadvantage:
By providing all relevant (cyclic) historic process data, these data can be used as input parameters for offline calculations. Both online and offline calculations are based on the same mathematical model core. On the one hand, this approach enables the evaluation and verification of new model parameters based on historic data. On the other hand, model parameters can be adapted quickly and efficiently. Above all, this approach represents the so-called digital twin and the possibility to optimize processes easily and quickly without affecting the current production.
Based on our understanding of the metallurgical fundaments and our expertise and experience in model and software development, we can – amongst others – offer the following metallurgical models and applications for the steel industry. Model development for other metallurgical industries (such as nonferrous, etc.) is available upon request.
Raw Material Optimization (RMO)
Depending on the alloy content of the grade to be produced, the share of raw material costs (scrap, alloying materials and metals) can be up to 60% of the total production costs .
In order to minimize raw material costs and to avoid deviations from analyses, the RMO software application offers the opportunity to determine the demand for raw materials for the planned material mix of a production period.
Electric Arc Furnace (EAF)
The off-line model calculates the melting of the scrap (fraction of solid and liquid), the melt temperature as well as the chemical composition of melt and slag based on a definable melting pattern and the charged scrap type and mass.
The on-line model calculates the parameters mentioned above based on real data recorded during the process.
Ladle Furnace (LF)
The off- and on-line application for the LF calculate the heat gain due to heating (arcing), the heat losses through the refractory lining and the surface (radiation), heat losses by inert gas stirring as well as temperature changes by material addition. The thermochemical and thermodynamic models (particularly desulphurization and deoxidation) account for the composition changes of slag and melt.
Vacuum Degassing (VD)
The computational metallurgy application for vacuum degassing (off- and on-line) incorporates the heat balance model (including the additional heat change due to decreased pressure) as well as the thermochemical and thermodynamic models, i.e., the kinetic models of degassing of hydrogen and nitrogen.
Argon Oxygen Decarburization (AOD)
Two models are available for the AOD process:
(1) calculation of the optimal blowing pattern (semidynamic)
(2) dynamic calculation of the current state of the melt, slag and temperature. For this purpose, the approach of the holistic model combines the heat balance model, the thermochemical and the thermodynamic model, the decarburization model (kinetic approach) and the models for the calculation of reduction materials and slag formers.
Vacuum Oxygen Decarburization (VOD)
Basically, the VOD model is similar to the AOD model as both processes are based on lowering the partial pressure of CO. A semidynamic approach calculates the optimal blowing pattern and the various chemical and physical submodels calculate the current condition of the melt throughout the VOD process.
Contact us directly for more information about process models for the primary and secondary metallurgy or other metallurgical issues in the field of mathematical modeling.
Casting and Solidification
We can look back on over 20 years of experience in experimental and model-based solidification analysis. In order to be able to analyze and calculate the solidification processes, regardless of which boundary conditions (ingot casting, continuous casting, sand casting), a wide variety of phenomena, from micro- to macroscopic orders of magnitude, must be understood. With our metallurgical understanding of all these different phenomena, our competence in numerical simulation and practical experience, we deliver intelligent applications as well as comprehensive analyzes and concepts in the design phase for solidification processes as well as process models for an integration into existing systems.
Thermophysical Material Properties
In order to obtain reliable results when performing numerical simulations of solidification processes, accurate thermophysical material properties are required. Typical required data are density, heat capacity and thermal conductivity as a function of temperature. Other important input variables are the phase transition temperatures and the associated latent heat.
Our models for calculating this data are suitable for both low and high alloy steels and stainless steels with chromium and nickel contents up to 30 wt.-% and 24 wt.-%, respectively. p>