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<p>Computer aided image analysis offers potential for sorting heterogeneous particles—especially in the waste-management sector, where traditional manual sorting reaches its limits. With the growing demand for high-quality steel scrap, partly due to the transition from blast furnaces to electric arc furnaces, the pressure to process metallic waste more precisely and at scale is increasing. The FFG flagship project Kiramet addresses this challenge by developing AI-based image processing for scrap sorting. This article shows which methods are particularly suitable for this purpose and how, in industry-oriented trials, they have already achieved iron fractions with purities exceeding 99 wt%.</p>

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Kiramet: KI-basierte Konditionierung von Schredderschrott – für grüneren Stahl

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Gerald Koinig,
  • Alexia Tischberger-Aldrian

摘要

Computer aided image analysis offers potential for sorting heterogeneous particles—especially in the waste-management sector, where traditional manual sorting reaches its limits. With the growing demand for high-quality steel scrap, partly due to the transition from blast furnaces to electric arc furnaces, the pressure to process metallic waste more precisely and at scale is increasing. The FFG flagship project Kiramet addresses this challenge by developing AI-based image processing for scrap sorting. This article shows which methods are particularly suitable for this purpose and how, in industry-oriented trials, they have already achieved iron fractions with purities exceeding 99 wt%.