Detection of copper-containing scrap in a post-shredder fraction with machine vision and artificial intelligence towards green-steel production

Copper contamination in scrap input for electric arc furnaces (EAF) causes quality issues in steel production, as copper cannot be removed during melting in EAFs and leads to surface cracking and brittleness in the final products. This work presents a cost-effective AI-based classification and sorting method using single-stage AI detectors to identify copper particles in post-shredder scrap, offering an alternative to X-ray fluorescence or laser-induced breakdown spectroscopy. After comparing all sizes of YOLOv8 and YOLOv11, the most promising architecture was further subjected to pruning, hyper parameter optimisation and conversion to decrease its inference latency without compromising its prediction accuracy. The thus generated YOLOv8n model achieved a worst-case inference time of 75 ms/image on CPU testing hardware with a mAP50–95 of 77 %. In terms of object-based accuracy, the testing on the independent test data set resulted in 89 % of all copper particles and 86 % of all iron particles being correctly identified. After these offline tests, a prototype consisting of a conveyor belt, low-cost industrial GPU, an industrial camera and an industrial high pressure nozzle bar was built to gauge the model's deployment into an industrial setting by using hyperparameter tuning and conversion to GPU optimised formats. On this, three-stage separation trials with throughputs ranging from 2.5 t/h to 10 t/h with initial copper contents of 10 % and 25 % were conducted. These trials resulted in a purity of the iron fraction of over 99.3 %, calculated by taking the mass of all copper containing particles in the iron fraction.