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This paper proposes an innovative framework based on artificial intelligence techniques to optimize the reuse of End-of-Life (EoL) ICT components. The system integrates two complementary modules: (1) zero-shot identification and classification of mechanical components from images using Segment Anything Model (SAM) and Contrastive Language-Image Pretraining (CLIP); (2) reuse profitability assessment through a Recovery Profitability Index (RPI) and Transformer models. Experimental results demonstrate an identification accuracy of 78% for the zero-shot module and up to 91% for profitability classification, highlighting the potential of this approach in the context of circular economy and industrial sustainability.

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An Intelligent Framework for End-of-Life ICT Components Valorization: a Zero-Shot Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Luca Di Angelo,
  • Paolo Di Stefano,
  • Valentino Grossi,
  • Emanuele Guardiani,
  • Antonio Marzola

摘要

This paper proposes an innovative framework based on artificial intelligence techniques to optimize the reuse of End-of-Life (EoL) ICT components. The system integrates two complementary modules: (1) zero-shot identification and classification of mechanical components from images using Segment Anything Model (SAM) and Contrastive Language-Image Pretraining (CLIP); (2) reuse profitability assessment through a Recovery Profitability Index (RPI) and Transformer models. Experimental results demonstrate an identification accuracy of 78% for the zero-shot module and up to 91% for profitability classification, highlighting the potential of this approach in the context of circular economy and industrial sustainability.