qontrol MAPS

Self-learning scrap chemistry and metallic yield

Real scrap chemistry and metallic yield are continuously derived from actual production data. Instead of relying on static master data, the system uses a self-learning raw material model that improves the predictability of melt chemistry with every heat, reducing out-of-specification risk and the need for conservative safety buffers.

Combined scrap, DRI/HBI, alloying and energy optimization

Scrap, DRI/HBI, alloy additions and energy consumption are optimized in one integrated approach. By considering actual metallurgical performance, balancing chemical and electrical energy consumption, and downstream process impact, it identifies the most cost-effective and technically robust raw material strategy across the full production route.