@inproceedings{e9cb506f3f9d4544a5b16cc0d8e2378c,
title = "Digital Solution for Optimizing Scrap Yard Management",
abstract = "SCOT is proposed as a technical solution for automated classification, stock control and characterization of the raw material in steelmaking scrap yards. SCOT improves the management of scrap as a key raw material in this sector through the digitalization of the process. Several cameras and sensors are integrated into existing scrap yard assets and combines the feedback from the devices with deep learning models (prepared to detect the areas of interest of each image and classify the type of scrap they contain) and a modular management software. This software manages the information generated by the different subsystems and performs sensor data fusion to identify the scrap material movements with reduced hardware requirements. The full system has already been installed in several industrial plants from ArcelorMittal and it has proven to be an essential tool for the characterization and control of the raw material in the scrap yard, capable of estimating the residual content of the classified scrap by analyzing the EAF process and optimizing the main scrap management operations.",
keywords = "automatic classification, bucket charge, digitalization, residual content, scrap inventory, scrap management, scrap yard, steelmaking, stock control",
author = "{Rodr{\'i}guez D}, J. and A. Vicente and A. Galletebeitia and R. Jaras and G. Sorrosal and {Arteche V}, {J. A.} and {Lago R}, A.",
note = "Publisher Copyright: {\textcopyright} 2023 by the Association for Iron & Steel Technology.; AISTech 2023 Iron and Steel Technology Conference and Exposition ; Conference date: 08-05-2023 Through 11-05-2023",
year = "2023",
doi = "10.33313/387/082",
language = "English",
series = "AISTech - Iron and Steel Technology Conference Proceedings",
publisher = "Association for Iron and Steel Technology",
pages = "723--733",
booktitle = "AISTech 2023 - Proceedings of the Iron and Steel Technology Conference",
}