Production of EAF steels with low content of N2 and S through vacuum treatment

2004 
The major aim of this project was to define the metallurgical and economical limits of a vacuum treatment applied to steels produced via EAF in order to reduce both nitrogen content below 50 ppm and sulphur content under 20 ppm, and to simulate this treatment by a dynamic modelling. The partners involved in this project were two steel producers (SIDENOR-Basauri (research performed by SIDENOR I+D), and ProfilARBED-Differdange (research performed by ProfilARBED Research (PARE)), and three institutes (BFI, IRSID, and MEFOS). Essentially, laboratory, pilot and plant experiments have been carried out and used to validate three different models (process, physical and CFD) that have been developed to simulate both N removal and desulphurisation reactions. These models predict the final content in nitrogen and sulphur of EAF steels processed under a defined vacuum treatment. Some of these models can be applied to improve the on-line process observation within a process control system. During the project the steel plants were responsible for carrying out industrial tests and for defining the limits of vacuum tank degassing applied to EAF steel, while the institutes were responsible for development and adaptation of the different models to simulate vacuum treatment. The main activities performed during the project can be summarised as follows: - Review of kinetic and thermodynamic aspects of N removal and desulphurisation reactions. - Fundamentals of simulation models. - Industrial trials at ProfilARBED Differdange (PADI) and SIDENOR Basauri. For collection and provision of necessary data for model simulation and verification. - Progressive development, adaptation and extension of dynamic process models by BFI, IRSID and MEFOS. - General investigations on main influence parameters for N removal and desulphurisation. - Pilot trial in a vacuum ladle of hydrogen injection for N removal. The following objectives of the research work have been reached: - The degassing behaviour with respect to N removal was simulated with sufficient accuracy for all tank degassing plants. Referring to the N removal efficiency, the predictions with the three models (process model (BFI), physical model (IRSID) and CFD model (MEFOS)) were in good agreement with the industrial results at PADI and SIDENOR, with an error standard deviation close to the standard dispersions obtained in chemical analysis. - The relative simulation error of the final hydrogen content was quite high, but for the objectives of the project the dehydrogenation simulation was only required to cover the influence on the nitrogen partial pressure. - The desulphurisation behaviour was simulated with good accuracy for the two degassing plants with the process model (BFI) on the basis of analysis values for the slag composition before vacuum. When using the results of a BFI slag calculation instead of the slag analysis, the model accuracy was deteriorated significantly. The reason is that within the slag calculation several assumptions have to be made, e.g. concerning the amount of carry-over slag and the amount of removed slag during deslagging. - The use of the original and measured chemical analysis, with respect to the initial sulphur content in the slag, gave poor results regarding the simulation of desulphurisation behaviour of PADI heats with the CFD model (MEFOS). However simulations with lower initial sulphur values in slag (not as closer to the sulphur saturation) gave much better results. More work is considered necessary to improve the results of the simulations, especially regarding the predicted final aluminium concentration in the steel. The modelling of the alumina activity has a predominant effect on the results. - A statistical desulphurisation model of PARE gave a good accuracy for the PADI heats quantifying the dependence of final sulphur content on slag composition.
    • Correction
    • Cite
    • Save
    • Machine Reading By IdeaReader
    0
    References
    0
    Citations
    NaN
    KQI
    []