Artificial Intelligence in Ironmaking and Steelmaking

Erscheinungsjahr 2027
ISBN: 978-3-032-34705-3
Verlag: Springer

Buch, Englisch, 765 Seiten, Format (B × H): 155 mm x 235 mm

This book describes the revolution taking place in production of iron and steel through the introduction of artificial intelligence, covering issues from mining to final products quality. It explains data analysis that automates the creation of analytical models based on the idea that systems may learn from data, recognize patterns, and make decisions with minimal human intervention in the context of an array of problems in the production of iron and steel. Specifically, the author presents an AI approach for efficient iron ores mining, machine learning-based and artificial intelligence in sintering operations, coke making, blast furnace and ladle operation,basic oxygen furnaces, direct reduced iron, electric arc furnaces, continuous casting monitoring and control, hot and cold rolling monitoring and control, and quality improvement in heat treatment processing.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Cavaliere, Pasquale

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Materialwissenschaft: Elektronik, Optik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Bauingenieurwesen Baukonstruktion, Baufachmaterialien
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik
• Naturwissenschaften Physik Thermodynamik Festkörperphysik, Kondensierte Materie

Inhaltsverzeichnis

Machine Learning Tools for Ironmaking and Steelmaking.- The impact of artificial intelligence on decarbonization in the steel industry.- Artificial Intelligence tools for Energy Efficiency in Ironmaking and Steelmaking.- Artificial Intelligence tools for Quality control in Ironmaking and Steelmaking.- AI approach for efficient iron ores mining.- Machine learning-based artificial intelligence in Sintering operations.- Machine learning-based artificial intelligence in Coke making operations.- Machine learning-based artificial intelligence in Blast Furnace.- Machine learning-based artificial intelligence in Ladle operations.- Machine learning-based artificial intelligence in BOF operations.- Machine learning-based artificial intelligence in DRI production.- Machine learning-based artificial intelligence in EAF operations.- Machine learning-based artificial intelligence for Continuous Casting Monitoring and control.- Machine learning-based artificial intelligence for Hot and Cold Rolling Monitoring and control.- AI approach for quality improvement in heat treatment processing.

Über Autor(innen)

Pasquale Daniele Cavaliere is Professor of Process Metallurgy at the University of Oulu, where he leads research on hydrogen-based steelmaking, industrial decarbonization, and advanced metallurgical processes. He holds a PhD in Materials Engineering from the University of Rome “Tor Vergata” and previously served for more than twenty years at the University of Salento as Assistant and Associate Professor. From 2005 to 2007 he was a Marie Curie Fellow at the Massachusetts Institute of Technology (MIT), a formative period that strengthened his international research trajectory in materials science and metallurgy. His scientific production includes more than 430 publications, over 170 peer-reviewed journal papers, and 10 books. He is consistently ranked among the Top Cited Scientists worldwide in the Elsevier–Stanford University global ranking. Cavaliere’s editorial activity spans more than forty international journals. He serves as Editor of the International Journal of Cast Metals Research, Associate Editor of several engineering journals, and Deputy Editor-in-Chief of Ironmaking & Steelmaking. He is a frequent keynote and plenary speaker at major international conferences across Europe, Asia, the Americas, and Australia, with more than fifty invited lectures on hydrogen reduction, steel decarbonization, hydrogen embrittlement, powder metallurgy, and advanced manufacturing. He maintains long-term collaborations with universities and research centers worldwide, including institutions in Poland, Canada, Brazil, China, Iran, Ireland, Spain, the UK, and the USA. He is a Fellow of IOM3 (UK).