Optimizing power consumption for electric arc furnace (EAF) has a critical impact for maximizing productivity. To achieve the goal, we propose an AI based algorithm that determines optimal timing for recharging scrap to EAF. More specifically, we predict power consumption and time duration required for melting scrap considering scrap types and amounts of each type of scrap. Furthermore, with the advance in explainable AI, we offer guidance for the optimal timing of recharging scrap. We evaluate the performance on a real site and successfully reduce scrap charging time of 3% and power consumption of 7.1%, 53,802 Japanese Yen.
EAF, Optimization, Cost-Saving, AI, Time-Series, XAI
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