Dr. Lucia Grizzaffi Profile

Dr. Lucia Grizzaffi

at Thales Alenia Space

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Proceedings Article | 28 May 2024 Presentation + Paper

Hyperspectral imaging-based approach for recycling space waste

Giuseppe Bonifazi, Idiano D'Adamo, Lucia Grizzaffi, Roberta Palmieri, Silvia Serranti, Antonia Simone

Proceedings Volume 13083, 130830E (2024) https://doi.org/10.1117/12.3018778

KEYWORDS: Hyperspectral imaging, Principal component analysis, Polymers, Near infrared, Data modeling, Plastics, Sustainability, Statistical analysis, Space operations, Statistical modeling

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The recycling of space debris is being prioritized more and more by international space agencies as a critical waste reduction method enabling the reuse of materials that would otherwise be wasted. Space missions typically generate various types of waste, including metals, nonmetals (such as plastics, foams, and packaging materials), and liquids (such as water, beverages, and chemicals). Reusing and/or recycling offers a sensible compromise between monetary gain and technological advancement. The potential for implementing the entirety of the recycling process and/or a portion of it directly in the space environment can be a fascinating and practical possibility. For the proper implementation of a recycling operation for materials and/or for the selection of production equipment (such as shredders, classifiers, separators, extruders, etc.) to be used in space, material identification and categorization are crucial. In this context, the possibility to use a sustainable low-cost strategy based on HyperSpectral Imaging (HSI) approaches was explored. In more detail, the possibility to classify plastic space debris by HSI sensors working in the Near InfraRed (NIR) range to recognize the presence of different polymer type for sorting/classification purposes was thus investigated in this study. The achieved results are very promising for the further development of the research activities, showing as the adopted HSI approach can be profitably utilized to identify, recognize, and classify the different material categories. This study was carried out in the framework of “Hyperspectral based sensing architectures for resource circularity project” belonging to the Spoke 5 - Closed-loop, sustainable, inclusive factories and processes of the Extended Partnership "MADE IN ITALY CIRCOLARE E SOSTENIBILE” (MICS) of the Italian PNRR.

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