Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches

Díaz‐Romero, Dillam Jossue; Eynde, Simon Van den; Zaplana, Isiah; Zhou, Chuangchuang; Sterkens, Wouter; Goedemé, Toon; Peeters, Jef

doi:10.1016/j.resconrec.2023.106865

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2024

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Cited by 16 publications

References 40 publications

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AI-Driven EoL Aircraft Treatment: A Research Perspective

Amirnia,

Keivanpour

2024

Lecture Notes in Networks and Systems

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AI-Driven EoL Aircraft Treatment: A Research Perspective

Amirnia,

Keivanpour

2024

Lecture Notes in Networks and Systems

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Individual model identification of waste digital devices by the combination of CNN-based image recognition and measured values of mass and 3D shape features

Koyanaka,

Kobayashi

2024

J Mater Cycles Waste Manag

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Classification of Automotive Aluminum Scrap into Cast and Wrought Alloys via Particle Size Analysis

Shankar,

Söderhjelm,

Apelian

2024

J. Sustain. Metall.

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The Aluminum (Al) industry is currently faced with the challenge of significantly reducing the environmental impact of Al production. Secondary Al or Al produced from Al scrap offers a beneficial pathway to lowering the production carbon footprint and energy consumption while transitioning to a circular economy business model. However, mixing of Al scrap streams and introduction of impurity elements at end of life lead to “downcycling” of Al alloys. Downcycling of Al alloys has been the norm in the automotive recycling industry. Automotive Al scrap from auto shredders dubbed “Twitch,” is an amalgamation of different Al alloys, cast as well as wrought alloys. Twitch is traditionally downcycled to low-value Al alloys due to the complexities in separating the Twitch into various alloy families as well as difficulties in blending and producing high-value Al alloys from scrap. This paper provides a framework and an approach that uses size gating, mass, and image analysis to separate the wrought and cast alloys within the Twitch feedstock and provides a simpler and a cost-effective means of separation of the scrap stream. Graphical Abstract

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Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches

Cited by 16 publications

References 40 publications

AI-Driven EoL Aircraft Treatment: A Research Perspective

AI-Driven EoL Aircraft Treatment: A Research Perspective

Individual model identification of waste digital devices by the combination of CNN-based image recognition and measured values of mass and 3D shape features

Classification of Automotive Aluminum Scrap into Cast and Wrought Alloys via Particle Size Analysis

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