An AI-powered holistic system for optimizing the usage of steel scrap in steel production
Time: Fri 2025-03-28 09.00
Location: F3 , Lindstedtvägen 26-28
Video link: https://kth-se.zoom.us/j/68692480767
Language: English
Subject area: Materials Science and Engineering
Doctoral student: Michael Schäfer , Processer
Opponent: Prof. Dr.-Ing Jan Conrad, Hochschule Kaiserslautern
Supervisor: Universitets lektor Björn Glaser, Processer; Affiliated Professor Herbert Köchner, Processer
Abstract
The steel industry is currently in a transformation process in order to be able to produce in a more environment-friendly way in the future. The secondary raw material steel scrap plays a crucial role in this transformation, as recycling scrap in the manufacturing process is environmentally friendly and sustainable. However, the increased use of steel scrap in the steel industry involves new challenges. Processes must be changed, product quality must be maintained and the increased throughput and demand for scrap must be managed. Digitalization and the use of AI technologies can help to optimize and automate the new processes.
When using AI in an industrial environment, there is often the challenge that not enough data of sufficient quality is available. In order to close this gap, a new freely available dataset of European scrap classes, was created and used in this work by applying a novel tilling technique. The creation and even more the annotation of such domain-specific datasets requires a lot of time and expert knowledge. For this reason, a self-supervised approach was implemented using different types of augmentations to extract the fine-grained structures typical for intrinsic disordered objects such as steel scrap. These results were used to control the scrap input as well as the scrap usage and thus automate the process.
The scrap used in the steel production process usually varies in origin and composition, which makes the compilation more difficult. When compiling the scrap mix, steel producers often rely on experience or have to carry out complex trials. A machine learning approach was implemented that can be used to simulate and optimize different scrap compositions. Based on these models, a new approach was developed to estimate the chemical content of the input materials used from standard process parameters without the use of additional sensors.
The integration of AI-models in a heterogeneous industrial environment is a major challenge. Ambient infrastructure needs to be adapted or created as required.To enable the various solutions to be embedded, the different machine learning technologies were combined, required infrastructure was set up as required and online models and interfaces were implemented for productive use.
In summary, this thesis presents an AI-powered holistic system that combines various technologies, optimizes steel scrap processes, and automates the scrap workflow from scrap entry to the end of the basic oxygen furnace process.