The microstructure of a steel (i.e. how single particles are organised) strongly affect physical properties of steel, such as strength, toughness, ductility, hardness, corrosion resistance and wear resistance. The microstructure of steel is determined at the very beginning of the production process, in a process phase called casting, in which molten metal is solidified into a semi-finished slab.
As the microstructure of steel has a great affect steel properties and end quality it is in the interest of steel makers to monitor and control the microstructure of steel during the production process. In FIMECC SIMP program advanced computational simulation models were developed to control and monitor the casting process. The developed models are integrated together to one online concept. The system simulates the important phenomena in continuous casting online, predicts the quality and can be used for online control.
The outcome of this new concept is that the steel industry can control much better the continuous casting process and steel quality than before. With online simulation models the quality of the end product can predicted in the early stage of processing. This results in saving in energy, time and raw material by improving the quality and decreasing the waste. The models can also be used off-line to design the manufacturing concepts of new advanced steel grades and solve their manufacturing problems. This decreases significantly the time needed to bring new or “new for the plant” products into production as all the critical process parameters can be determined beforehand with simulators without time consuming and laboratory and production trials.
As the requirements for steel quality from customers become all the time stricter and the energy efficiency, productivity and ecological aspects are of increasing improved computer control of steelmaking processes and steel quality are needed. Advances computational simulation models help to solve practical problems in industrial casters and to improve process practices and control.
The solution take steel industry to digital era linking online information to product quality enabling online control and quality prediction. Moreover, with the help of the new solution process can be improved by designing the optimal process parameters to avoid defects and improve the quality and designing the production parameters for new advanced steels. This results in improved yield and energy savings.
The concept can be applied by in SSAB Raahe and Outokumpu Tornio. The work is now on the level “proof of concept”, i.e. the aim has been to show and verify its feasibility. The system is running online but how well the quality can be predicted will finally depend on the coming validation.
By improving the control and quality prediction in steel plants, many kinds of quality and practical problems can be solved as stated before. And if it is possible to get fast feedback concerning steel quality, corrective actions can still be done when steel is still in processing phase. These all bring clear benefits for industry in the form of monetary and energy savings.
Improved quality decreases quality costs and increases productivity as products can be made “right at the first time”. It results in increased cost efficiency and yield and energy savings. The simulators enable also the design of the manufacturing concepts of new advanced steel grades. This reduces the need for production trials with significant carbon dioxide emissions and decrease time to the market.
Seppo Louhenkilpi, Aalto University ja Jukka Laine, Casim Consulting
SSAB Europe, Outokumpu Stainless, Casim Consulting, Aalto University