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Recycling-Oriented Characterization of Space Waste Through Clean Hyperspectral Imaging Technology in a Circular Economy Context

Author

Listed:
  • Giuseppe Bonifazi

    (Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, 00184 Rome, Italy)

  • Idiano D’Adamo

    (Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy)

  • Roberta Palmieri

    (Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, 00184 Rome, Italy)

  • Silvia Serranti

    (Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, 00184 Rome, Italy)

Abstract

Waste management is one of the key areas where circular models should be promoted, as it plays a crucial role in minimizing environmental impact and conserving resources. Effective material identification and classification are essential for optimizing recycling processes and selecting the appropriate production equipment. Proper sorting of materials enhances both the efficiency and sustainability of recycling systems. The proposed study explores the potential of using a cost-effective strategy based on hyperspectral imaging (HSI) to classify space waste products, an emerging challenge in waste management. Specifically, it investigates the use of HSI sensors operating in the near-infrared range to detect and identify materials for sorting and classification. Analyses are focused on textile and plastic materials. The results show promising potential for further research, suggesting that the HSI approach is capable of effectively identifying and classifying various categories of materials. The predicted images achieve exceptional sensitivity and specificity, ranging from 0.989 to 1.000 and 0.995 to 1.000, respectively. Using cost-effective, non-invasive HSI technology could offer a significant improvement over traditional methods of waste classification, particularly in the challenging context of space operations. The implications of this work identify how technology enables the development of circular models geared toward sustainable development hence proper classification and distinction of materials as they allow for better material recovery and end-of-life management, ultimately contributing to more efficient recycling, waste valorization, and sustainable development practices.

Suggested Citation

  • Giuseppe Bonifazi & Idiano D’Adamo & Roberta Palmieri & Silvia Serranti, 2025. "Recycling-Oriented Characterization of Space Waste Through Clean Hyperspectral Imaging Technology in a Circular Economy Context," Clean Technol., MDPI, vol. 7(1), pages 1-14, March.
    • Handle: RePEc:gam:jcltec:v:7:y:2025:i:1:p:26-:d:1612228
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    References listed on IDEAS

    as
    1. Chao-Hui Feng, 2024. "Colour Analysis of Sausages Stuffed with Modified Casings Added with Citrus Peel Extracts Using Hyperspectral Imaging Combined with Multivariate Analysis," Sustainability, MDPI, vol. 16(19), pages 1-14, October.
    2. Roberta Palmieri & Riccardo Gasbarrone & Ludovica Fiore, 2023. "Hyperspectral Imaging for Sustainable Waste Recycling," Sustainability, MDPI, vol. 15(10), pages 1-3, May.
    3. Meena Malik & Sachin Sharma & Mueen Uddin & Chin-Ling Chen & Chih-Ming Wu & Punit Soni & Shikha Chaudhary, 2022. "Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models," Sustainability, MDPI, vol. 14(12), pages 1-18, June.
    4. Hondroyiannis, G. & Sardianou, E. & Nikou, V. & Evangelinos, K. & Nikolaou, I., 2024. "Circular economy and macroeconomic performance: Evidence across 28 European countries," Ecological Economics, Elsevier, vol. 215(C).
    Full references (including those not matched with items on IDEAS)

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