AI helps steelmakers optimize scrap sorting to slash costs and emissions
Researchers have developed machine learning models that automatically analyze scrap metal composition before furnace melting, eliminating time-consuming manual selection. The advancement could significantly reduce production costs and energy consumption for European steel producers, while improving competitiveness as the industry faces mounting climate regulations.
Originaltitel: Unlocking the objective of energy efficient steel-making by robust scrap melting with the help of advanced algorithms
Electric Arc Furnace (EAF) steelmaking is critical to the European steel industry, and optimising the process is key to achieving highefficiency, low-cost, and high-quality steel production. However, the current method of determining the optimal bucket charge composition for the EAF is complex and time consuming, and may not take into account the availability of scrap in the scrapyard. This report investigates statistical and mathematical models of the EAF process along with Artificial Intelligence (AI) approaches to classifying images of the scrap in a bucket. Ultimately, this work contributes to improved efficiency, reduced costs, and better quality steel. The outcomes of the work will also contribute to the broader conversation around to the fight against climate change and the European steel industry competitiveness by applying highly innovative methods and technologies to the steel sector.