A Novel Approach to Energy Management in Electric Steelworks

Electric steelworks are a paradigmatic representation of the concept of circular economy, as it recycles steel components at the end‐of‐life products. Moreover, its importance is foreseen to grow according to the increasing demand of decarbonizing steel production to meet the ambitious goals of the European Green Deal. The electric arc furnace‐based route is still characterized by a limited diversification of energy supply sources thus, managing the three factors of smart energy management, energy prices, and production planning can be jointly considered as a crucial leverage for reducing production costs while ensuring satisfaction of energy demand coming from the different processes and developing digital approaches and tools to implement the fast adaptation to power grid behaviour. The article describes a novel energy management system based on innovative components and a flexible infrastructure, which uses machine learning and an optimization approach to minimize electricity consumption and level trends by matching intelligent production planning and power grid offer and related energy costs. The developed solution and the set of neural networks‐based models estimating electricity consumption in electric arc furnace and ladle furnace based on production information are described. The models were trained and validated using production and process data from a real steelworks.