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think.steel 4 2023
Another year of innovation in steel

Any year is a year of innovation in steel. Innovation of technology, but also innovation of mill management structures, and of the repercussions on the community that forms the social environment of a steelworks. Once a year, the World Steel Association (“worldsteel”) takes stock of merits in low-carbon steel production , sustainability, life cycle assessment, and other criteria, to be selected for the annual “Steelie Awards”. The winners were announced at a ceremony in Shanghai on 16 October. Now, let us remember George C. Scott, the actor who once would not accept the Hollywood Oscar on the basis that performances could not be compared to one another. Similarly, any of the steel innovations that found their way on the shortlist deserve a mention and a description, so think.steel is presenting the full portfolio of proposals on 18 pages that form the core of this issue.

Another focus has been provided from worldsteel as well: The association’s regular Open Forum conference, this year with the subtitle “Ciimate Action”. It was staged in September in Antwerp, where company executives and independent specialists discussed the changing self-concept within the industry, but also its perception from the outside.

Furthermore in this issue, Primetals is unveiling secrets tin the caster mold with fiber technology, a US scrap expert asks “How green are your feedstocks?,” and the Fraunhofer Institute for Mechanics of Materials IWM explains ways of using deep learning to classify steel materials objectively.

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Unsere Top Themen

Steelie Awards
  • worldsteel’s 14th Steelie Awards – the winners, the nominees
  • worldsteel Forum highlights steel image change
  • Global DRI production sets new annual record in 2023

Weitere Themen

Unveiling secrets in the caster mold with fiber technology: Interview with Martin Schuster and Nicole Oberschmidleitner, Researchers at the Austrian offices of Primetals Technologies


How Green Are Your Feedstocks?

Jeremy Jones, CiX

Using deep learning to classify steel materials objectively

Ali Riza Durmaz, Sai Teja Potu, Daniel Romich, Johannes J. Möller, Ralf Nütz

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