Steel 4.0

This book addresses digitalization and the steel industry. It is written from the perspective of both industrial engineering and data science and discusses digitalization problems, novel production planning and scheduling algorithms in ironmaking and steelmaking, machine learning applications and opportunities in steel plants and quality-related algorithms in steel plants.

Even though digitalization is an important trend in steel industry, current contributions mainly focus on lower automation levels, i.e., field and control levels, that are mainly covered in mechanical, electrical and material engineering domains. On higher planning levels, there are hardly comprehensive scientific contributions on intelligent digitalization issues available. This book fills this gap.

Collects novel digitalization approaches for individual process steps in steelmaking Contains artificial intelligence and machine learning-based prediction and planning and scheduling Includes advanced modeling and simulation approaches including digital process twins

Autorentext

Professor Dr. Dr.-Ing. Yilmaz Uygun studied Industrial Engineering at the FH Südwestfalen University of Applied Sciences and Logistics Engineering at the University of Duisburg-Essen. He earned a doctoral degree in engineering from TU Dortmund University/Fraunhofer IML and another one in logistics from the University of Duisburg-Essen. Afterward, he moved to the United States and joined the Industrial Performance Center of the Massachusetts Institute of Technology (MIT) to work as postdoctoral researcher. In 2016, he was appointed as a professor of Logistics Engineering, Technologies and Processes at Jacobs University Bremen (now Constructor University Bremen). Simultaneously, he is still a research affiliate at MIT. Throughout his career, he was involved in many basic and applied digitalization projects. Among his research interests is the development of novel approaches for production and logistics-related issues, such as inventory management, requirements management and scheduling.

Dr. Atilla Özgür earned his bachelor's degree in Electrical Engineering from Middle East Technical University (Ankara, Turkey) in 2003. After graduation, he worked as a professional software developer while continuing his academic studies. He earned his master's degree in Computer Engineering from Atilim University (Ankara, Turkey) in 2007 and his Ph.D. degree in Electrical Engineering from Baskent University (Ankara, Turkey) in 2017. His Ph.D. thesis was about applying machine learning and optimization methods to intrusion detection domain. In 2018, he joined Jacobs University Bremen (now Constructor University Bremen) as a postdoctoral researcher in a digitalization project in steel industry. For several years now, he is been applying his knowledge on machine learning, optimization and network analysis methods to steel production.

Professor Dr. Marc-Thorsten Hütt studied physics in Göttingen and Paris and received his Ph.D. in Göttingen in 1997. Following longer research stays in Novosibirsk, Paris, Warsaw and Darmstadt, he became an assistant professor of Theoretical Biology and Bioinformatics in 2001 at Darmstadt University of Technology. In 2006, he moved to Jacobs University (now Constructor University) in Bremen, accepting a Professorship in Computational Systems Biology. From 2000 to 2005, he was a member of "Die Junge Akademie," an institution founded by Berlin-Brandenburgische Akademie der Wissenschaften and Deutsche Akademie der Naturforscher Leopoldina. Since 2019, he is a member of the European Academy of Sciences and Arts. Among his research interests is the development of mathematical tools for analyzing spatiotemporal pattern formation and the relationship between topology and dynamics in complex networks.

Inhalt

Introduction.- Planning and Scheduling of Electric Arc Furnace based Steelmaking.- Systematic review of steel surface defect detection methods on the open access datasets of Severstal and the Northeastern University (NEU).- Decision Support Systems for Steel Production Planning State of the Art and Open Questions.- Volatility and Synchronization in Steel Manufacturing A Simulation Study of a Modern Steel Mill.- A Comparison of Crossover Operators in Genetic Algorithms for Steel Domain.- Comparative Study of Two Genetic Algorithms for Steel Production Planning under Different Order Backlog Circumstances.- Effect of PolyLoss function on Steel defect detection.- Novel genetic algorithm for simultaneous scheduling of two distinct steel production lines.