Deep Learning based Tool for Optimizing Scrap Feed prior to EAF Operation

Självklart! Här är texten med de saknade mellanslagen tillagda, utan andra ändringar: The steel industry is a key sector for modern society. Due to the extreme versatility of steel, it finds application in very different sectors. However, the steel industry is also very polluting, accounting for roughly 7-9 % of the yearly global anthropogenic CO2 emissions. Steel is produced either from iron ore, with the Blast Furnace-Basic Oxygen Furnace (BF-BOF) process, or from steel scrap, following the Electric Arc Furnace (EAF) process. With the latter being a more sustainable alternative (roughly half of the energy required and 1.5 tonnes of CO2 emissions), the steel industry looks forward to shifting the production towards the EAF, which today accounts for 29 % of the global production. However, differently from the BF-BOF, the EAF has still not been optimized up to its full potential. In the process of achieving EAF process’ maximum efficiency, the integration of artificial intelligence could represent a significant step forward. The power and computational ability of machine learning models may indeed be used to further analyse the data collected from industry. In this way, it could be possible to improve the understanding of the EAF process and help optimizing it. This thesis focuses on using data clustering algorithms (Gaussian Mixture Model and K-Means) to analyse real industrial data. The objective was to examine the influence of the steel scrap type and its preparation on the EAF energy consumption. The information gathered during the preliminary data analysis and the data clustering were then used to develop and support an optimizer tool used to predict the most energy-efficient configuration of the EAF charging bucket.