The design of industrial processes relies on accurate thermodynamic data, which CALPHAD (Computer Coupling of Phase Diagrams and Thermochemistry) enhances by developing models to predict properties in multicomponent systems and integrating data into consistent databases. This work is disseminated through an annual conference, welcoming high-quality contributions in computational and experimental thermodynamics, phase transformations, and materials design.

Daniel Kavic had a poster presentation entitled “Hybrid data-driven thermodynamically-based temperature modeling of secondary steelmaking” at the “51^th International Conference on Computer Coupling of Phase Diagrams and Thermochemistry”:

“The work presents a novel hybrid approach for predicting temperature during ladle furnace treatment. On the one hand, this method uses data-driven modeling of process-related boundary conditions. On the other hand, it incorporates the thermodynamic description of diverse chemical reactions within the ladle under adiabatic conditions using ChemApp^TM Python and FactSage^TM. The new hybrid temperature prediction method will be utilized by the in-house software “i-clean”.”

Daniel got the CALPHAD Best Poster Award for his work.

Find out more about CALPHAD 2024 by clicking here.