Abstract
The technological innovation and the relentless marketing of new electronic products with improved performance generate increasing quantities of Waste from Electrical and Electronic Equipment (WEEE). In this scenario, End-Of-Life (EOL) flat monitors and screens represent a category generating, as a consequence of the rapid change in technology, an important amount of waste. Considering future estimations, the implementation of an adequate recycling infrastructure is necessary. An efficient, reliable and low-cost analytical tool is thus needed to perform detection/control actions in order to assess: i) waste composition and ii) physical-chemical attributes of the resulting materials. The knowledge of these information is a requirement to set-up and to implement correct recycling actions.
In this study, a cascade identification approach, based on Near InfraRed (NIR) – HyperSpectral Imaging (HSI), was carried out. More in detail, a four-steps classification was designed, implemented and set-up in order to recognize different materials occurring in a specific WEEE stream: EOL milled monitors and flat screens. Adopting the proposed approach, different material categories are correctly recognized and classified. Obtained results can be useful not only to set-up a quality control system, but also to improve sorting actions in this specific recycling sector.
Zusammenfassung
End-of-Life (EOL) Flachbildschirme sind eine Produktkategorie, die infolge des raschen technologischen Wandels große Abfallmengen erzeugt. Mit Blick auf künftige Schätzungen für die Zahl dieser Produkte sind Recyclinginfrastrukturen unumgänglich. Hierfür wird ein effizientes, zuverlässiges und kostengünstiges Analysetool benötigt, um für die Bewertung von i) Abfallzusammensetzung und ii) physikalisch-chemischen Eigenschaften der Materialien Nachweis- und Kontrollmaßnahmen durchzuführen. Diese Informationen sind Voraussetzung für die Entwicklung und die Umsetzung angemessener Recyclingkonzepte.
In dieser Arbeit wurde ein stufenweiser Identifikationsansatz auf der Grundlage von Nahinfrarot (NIR) – Hyperspektraler Bildgebung (HSI) verfolgt. Insbesondere wurde eine vierstufige Klassifizierung entworfen, implementiert und zum Einsatz gebracht, um unterschiedliche Materialien von EOL Flachbildschirmen zu identifizieren. Mit dem vorgeschlagenen Ansatz werden verschiedene Materialkategorien richtig erkannt und klassifiziert. Die erzielten Ergebnisse können nicht nur für den Aufbau eines Qualitätskontrollsystems sondern auch für Sortieraufgaben in diesem spezifischen Recyclingsektor nützlich sein.
About the authors
Giuseppe Bonifazi is Full Professor of Raw Materials Engineering at the Department of Chemical, Materials and Environment (DICMA) at La Sapienza – University of Rome. He has an extensive experience over 35 years on i) material characterization, ii) development and set up of procedures for the identification of objects and material using pattern recognition techniques based on classical and hyperspectral imaging techniques and iii) analysis and the application of methodologies to study and model industrial processes with reference to particulate solids material. This activity is documented by more than 400 papers in international journals and conference proceedings to more than 50 international European and national research projects.
Riccardo Gasbarrone is a PhD candidate in electrical engineering, materials, and nanotechnology at La Sapienza – University of Rome. He graduated in environmental engineering at La Sapienza in 2016. His research activities are currently focused on innovative techniques and sensing applications for material characterization.
Roberta Palmieri is a research fellow at La Sapienza – University of Rome. She is a PhD engineer and she works at Raw Materials Unit of DICMA from 2013. Most of her current research is about raw materials and secondary raw materials characterization, using classical and innovative approaches, in particular hyperspectral imaging analysis.
Silvia Serranti is full professor at the Department of Chemical Materials Environment Engineering (DICMA) at La Sapienza – University of Rome. She is a PhD geologist and she has been working for 20 years in the Raw Materials Unit of DICMA. Her research activity is related to primary and secondary raw materials characterization and valorization, documented by more than 150 scientific papers in international journals and conference proceedings, and participating in 12 European research projects.
References
1. Ballabio, D. and Consonni, V. 2013. Classification tools in chemistry. Part 1: linear models. PLS-DA. Analytical Methods 5: 3790–3798.10.1039/c3ay40582fSearch in Google Scholar
2. Barker, M. and Rayens, W. 2003. Partial least squares for discrimination. Journal of Chemometrics 17: 166–173.10.1002/cem.785Search in Google Scholar
3. Bonifazi G., Palmieri, R. and Serranti, S. 2015. Hyperspectral imaging applied to End-OF-Life (EOL) concrete recycling. tm – Technisches Messen, 82 (12): 616–624.10.1515/teme-2015-0044Search in Google Scholar
4. Bonifazi G., Palmieri R. and Serranti S. 2015. Short wave infrared hyperspectral imaging for recovered postconsumer single and mixed polymers characterization. Proceedings of SPIE 2015, IS&T/SPIE Electronic Imaging 2015, San Francisco, California, United States, 8–15 February 2015.Search in Google Scholar
5. Bonifazi G., Palmieri R. and Serranti S. 2017. Concrete drill core characterization finalized to optimal dismantling and aggregates recovery. Waste Management, 60: 301–310.10.1016/j.wasman.2016.10.008Search in Google Scholar PubMed
6. Bonifazi, G., Gasbarrone, R. and Serranti, S. 2018a. Characterization of printed circuit boards from e-waste byproducts for copper beneficiation. Proceeding of ECOMONDO 2018, pp. 65–69.Search in Google Scholar
7. Bonifazi, G., Capobianco, G. and Serranti, S. 2018b. A hierarchical classification approach for recognition of low-density (LDPE) and high-density polyethylene (HDPE) in mixed plastic waste based on short-wave infrared (SWIR) hyperspectral imaging. Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy, 198: 115–122.10.1016/j.saa.2018.03.006Search in Google Scholar PubMed
8. Bonifazi, G., Palmieri, R. and Serranti, S. 2018c. Evaluation of attached mortar on recycled concrete aggregates by hyperspectral imaging. Construction and Building Materials, 169: 835–842.10.1016/j.conbuildmat.2018.03.048Search in Google Scholar
9. Bonifazi, G., Capobianco, G., Palmieri, R. and Serranti, S. 2019a. Hyperspectral imaging applied to the waste recycling sector. Spectroscopy Europe 3 (2).10.1255/sew.2019.a3Search in Google Scholar
10. Bonifazi G., Gasbarrone R., Palmieri R. and Serranti S. 2019b. Plastic identification from end of life flat monitors by hyperspectral imaging methods. Proceedings of Sardinia 2019, 17th International Waste Management and Landfill Symposium, Forte Village, Cagliari, Italy, 30 September–04 October 2019.Search in Google Scholar
11. Chancerel, P. and Rotter, S. 2007. Recycling oriented characterisation of WEEE. Proceedings of Eco-X Conference, Vienna, Austria, pp. 205–212.Search in Google Scholar
12. Farcomeni, A., Serranti, S. and Bonifazi, G. 2008. Non-parametric analysis of infrared spectra for recognition of glass and glass ceramic fragments in recycling plants. Waste Management, 28: 557–564.10.1016/j.wasman.2007.01.019Search in Google Scholar PubMed
13. Fawcett, T. 2006. An introduction to ROC analysis. Pattern Recognition Letters, 27 (8): 861–874.10.1016/j.patrec.2005.10.010Search in Google Scholar
14. Geladi, P., Grahn, H. and Burger, J. 2007. Multivariate images, hyperspectral imaging: background and equipment. In: (Grahn, H. and Geladi, P. Eds.) Techniques and Applications of Hyperspectral Image Analysis. John Wiley & Sons, West Sussex, England, pp. 1–15.10.1002/9780470010884Search in Google Scholar
15. Hyvarinen, T., Herrala, E. and Dall’Ava A. 1988. Direct sight imaging spectrograph: a unique add-on component brings spectral imaging to industrial applications. Proceedings of SPIE 3302.Search in Google Scholar
16. Hu, B., Serranti, S., Fraunholcz, N., Di Maio, F. and Bonifazi, G. 2013. Recycling-oriented characterization of polyolefin packaging waste, Waste Management, 33: 574–584.10.1016/j.wasman.2012.11.018Search in Google Scholar PubMed
17. Luciani, V., Bonifazi, G., Hu, B., Rem, P. and Serranti, S. 2015. Upgrading of PVC rich wastes by magnetic density separation and hyperspectral imaging quality control. Waste management, 45: 118–125.10.1016/j.wasman.2014.10.015Search in Google Scholar PubMed
18. Oliveira, C. R. D., Bernardes, A. M. and Gerbase, A. E. 2012. Collection and recycling of electronic scrap: A worldwide overview and comparison with the Brazilian situation. Waste Management, 17 (8): 1592–1610.10.1016/j.wasman.2012.04.003Search in Google Scholar PubMed
19. Palmieri R., Bonifazi G. and Serranti S. 2014. Recycling-oriented characterization of plastic frames and printed circuit boards from mobile phones by electronic and chemical imaging. Waste Management, 34 (11): 2120–2130.10.1016/j.wasman.2014.06.003Search in Google Scholar PubMed
20. Rinnan, Å., van den Berg, F. and Engelsen S. B. 2009. Review of the most common pre-processing techniques for near-infrared spectra. Trends in Analytical Chemistry, 28: 1201–1222.10.1016/j.trac.2009.07.007Search in Google Scholar
21. Robinson B. H. 2009. E-waste: An assessment of global production and environmental impacts. Science of the Total Environment, 408 (2): 183–191.10.1016/j.scitotenv.2009.09.044Search in Google Scholar PubMed
22. Salhofer, S., Spitzbart, M. and Maurer K. 2011. Recycling of LCD Screens in Europe –State of the Art and Challenges. In: (Hesselbach, J. and Herrmann, C., Eds.) Glocalized Solutions for Sustainability in Manufacturing. Springer, Berlin, pp. 454–458.10.1007/978-3-642-19692-8_78Search in Google Scholar
23. Serranti, S., Gargiulo, A. and Bonifazi, G. 2012a. Hyperspectral imaging for process and quality control in recycling plants of polyolefin flakes. Journal of Near Infrared Spectroscopy, 20: 573–581.10.1255/jnirs.1016Search in Google Scholar
24. Serranti, S., Gargiulo, A. and Bonifazi, G. 2012b. Classification of polyolefins from building and construction waste using NIR hyperspectral imaging system. Resources Conservation & Recycling, 61: 52–58.10.1016/j.resconrec.2012.01.007Search in Google Scholar
25. Serranti, S., Luciani, V., Bonifazi, G., Hu, B. and Rem, P. 2015a. An innovative recycling process to obtain pure polyethylene and polypropylene from household waste. Waste Management, 35: 12–20.10.1016/j.wasman.2014.10.017Search in Google Scholar PubMed
26. Serranti, S., Palmieri, R. and Bonifazi G. 2015b. Hyperspectral imaging applied to demolition waste recycling: an innovative approach for quality control. Journal of Electronic Imaging, 24 (4): 043003-1–043003-9.10.1117/1.JEI.24.4.043003Search in Google Scholar
27. Serranti, S., Trella, A., Bonifazi, G. and Izquierdo, C. G. 2018. Production of an innovative biowaste-derived fertilizer: Rapid monitoring of physical-chemical parameters by hyperspectral imaging. Waste Management, 75: 141–148.10.1016/j.wasman.2018.02.013Search in Google Scholar PubMed
28. Silva, E. J., Britto, A. S., Oliveira, L. S., Enembreck, F., Sabourin, R. and Koerich, A. L. 2017. A two-step cascade classification method. International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, pp. 573–580.10.1109/IJCNN.2017.7965904Search in Google Scholar
29. Suresh, S. S., Mohanty, S. and Nayak S. K. 2017. Preparation and characterization of recycled blends using poly(vinyl chloride) and poly(methyl methacrylate) recovered from waste electrical and electronic equipments. Journal of Cleaner Production 149: 863–873.10.1016/j.jclepro.2017.02.057Search in Google Scholar
30. Suresh, S. S., Mohanty, S. and Nayak, S. K. 2018. Preparation of poly (vinyl chloride)/poly(methyl methacrylate) recycled blends: effect of varied concentration of PVC and PMMA in stability of PVC phase on recycled blends. Proceedings of Materialstoday 5 (2): 8899–8907.10.1016/j.matpr.2017.12.324Search in Google Scholar
31. Tarantili, P. A., Mitsakaki, A. N. and Petoussi, M. A. 2010. Processing and properties of engineering plastics recycled from waste electrical and electronic equipment (WEEE). Polymer Degradation and Stability 95: 405–410.10.1016/j.polymdegradstab.2009.11.029Search in Google Scholar
32. UNEP. 2009. RECYCLING – FROM E-WASTE TO RESOURCES. s.l.: United Nations Environment Programme & United Nations University.Search in Google Scholar
33. Veit, H. M. and Bernardes, A. M. 2015. Electronic Waste: Generation and management. In: (Veit, H. M. and Bernardes, A. M., Eds.) Electronic Waste: recycling techniques. Springer International Publishing, Switzerland, pp. 3–10.10.1007/978-3-319-15714-6_2Search in Google Scholar
34. Zeng, X., Mathewes, J. A. and Li J. 2018. Urban Mining of E-Waste is Becoming More Cost-Effective Than Virgin Mining. Environmental Science & Technology 52 (8): 4835–4841.10.1021/acs.est.7b04909Search in Google Scholar PubMed
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