EAF Optimization
Optimize energy input, electrode consumption, and tap-to-tap time with hybrid AI models trained on your EAF process data.
on flagship site
of liquid steel
in real time
co-engineered with operators
What operators actually see on the furnace.
Anonymised screenshots from Industream deployed on a European Electric Arc Furnace. Refining-phase cockpit, steel-temperature prediction, Golden Heat detection, Schmelzgrad (meltdown degree) monitoring, hybrid-model error analysis.
Complete EAF Optimization
Energy Optimization
Predictive per-heat consumption models. Real-time recommendations to reduce kWh per tonne produced.
Electrode Management
Per-heat electrode consumption tracking and early drift detection before breakage. Predictive replacement scheduling.
Tap-to-Tap Optimization
Phase-by-phase tap-to-tap time analysis: charging, melting, refining. Identification of recurring bottlenecks.
Real-Time Dashboard
Operator dashboards with per-heat KPIs, baseline comparison, and configurable alerts on all process parameters.
Every Heat Depends on Operator Experience
EAF optimization relies on operator experience and manual adjustments. The result: energy waste, premature electrode wear, and production variability.
Costly Manual Adjustments
Charge recipes are manually adjusted heat by heat. Every mistake translates directly into energy cost and lost time.
Invisible Energy Waste
Without a per-heat consumption model, deviations from the optimum go unnoticed until the monthly bill arrives.
Non-Capitalized Expertise
Operator best practices are never formalized. When an expert leaves, performance drops.
Ready to Optimize Your EAF?
Book a demo and discover how InduStream reduces your costs within the first weeks.