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AI-Driven Real-Time Temperature Regulation From Steelmaking to Casting
In steelmaking, maintaining optimal superheat at the caster is paramount, necessitating precise temperature control. This study highlights an enhancement in temperature guidance for steel production at the BOF, SLD, LTS, and VD, facilitated by AI. This tool not only suggests temperature settings but also provides real-time predictions, significantly reducing time-intensive and costly heating procedures. This paper presents four data-driven global recommendation models and two local prediction models, all operating in real time. These models incorporate a comprehensive range of parameters, from net energy input and chemical additives to treatment processes, transport durations, and the thermal states of ladles and tundishes.
Mr. Nicholas Velto | Cleveland-Cliffs Burns Harbor
Dr. Michael Peintinger | Smart Steel Technologies Inc.