A waste separation system based on sensor technology and deep learning: A simple approach applied to a case study of plastic packaging waste

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dc.contributor.authorPučnik, Rokcs
dc.contributor.authorDokl, Monikacs
dc.contributor.authorFan, Yee Vancs
dc.contributor.authorVujanović, Annamariacs
dc.contributor.authorNovak Pintarič, Zorkacs
dc.contributor.authorAviso, Kathleen B.cs
dc.contributor.authorTan, Raymond Rcs
dc.contributor.authorPahor, Bojancs
dc.contributor.authorKravanja, Zdravkocs
dc.contributor.authorČuček, Lidijacs
dc.coverage.issueAprilcs
dc.coverage.volume450cs
dc.date.accessioned2025-06-17T06:56:22Z
dc.date.available2025-06-17T06:56:22Z
dc.date.issued2024-04-15cs
dc.description.abstractPlastic waste pollution is a challenging and complex issue caused mainly by high consumption of single-use plastics and the linear economy of "extract-make-use-throw". Improvements in recycling efficiency, behaviour changes, circular business models, and a more precise waste management system are essential to reduce the volume of plastic waste. This paper proposes a simplified conceptual model for a smart plastic waste separation system based on sensor technology and deep learning (DL) to facilitate recovery and recycling. The proposed system could be applied either at the source (in a smart waste bins) or in a centralised sorting facility. Two smart separation systems have been investigated: i) the one utilising 6 sensors (near-infrared (NIR), humidity, temperature, CO2, CH4, and a laser profile sensor) and ii) the one with an RGB camera to separate packaging materials based on their composition, size, cleanliness, and appearance. Simulations with a case study showed that for a camera-based sorting, Inception-v3, a DL model based on convolution neural networks (CNN), achieved the best overall accuracy (78%) compared to ResNet-50, MobileNet-v2, and DenseNet-201. In addition, the separation resulted in a higher number of misclassified items in bins, as it focused solely on appearance rather than material composition. Sensor-based sorting faced limitations, particularly with dark colouration and organic matter entrapment. Combining the information from sensors and cameras could potentially mitigate the limitations of each individual method, thus resulting in higher purity of the separated fractions.en
dc.formattextcs
dc.format.extent141762-141762cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationJournal of Cleaner Production. 2024, vol. 450, issue April, p. 141762-141762.en
dc.identifier.doi10.1016/j.jclepro.2024.141762cs
dc.identifier.issn0959-6526cs
dc.identifier.orcid0000-0001-5514-0260cs
dc.identifier.other197362cs
dc.identifier.researcheridH-1088-2019cs
dc.identifier.scopus57189519052cs
dc.identifier.urihttps://hdl.handle.net/11012/252844
dc.language.isoencs
dc.publisherELSEVIER SCI LTDcs
dc.relation.ispartofJournal of Cleaner Productioncs
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0959652624012101cs
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0959-6526/cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/cs
dc.subjectWaste managementen
dc.subjectSmart waste bin systemen
dc.subjectCentral post-sortingen
dc.subjectSensor technologyen
dc.subjectDeep learningen
dc.subjectConvolutional neural networksen
dc.titleA waste separation system based on sensor technology and deep learning: A simple approach applied to a case study of plastic packaging wasteen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-197362en
sync.item.dbtypeVAVen
sync.item.insts2025.06.17 08:56:22en
sync.item.modts2025.06.17 08:33:49en
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Laboratoř integrace procesůcs
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