Plastic waste categorisation using machine learning methods-metals contaminations

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU142154" target="_blank" >RIV/00216305:26210/21:PU142154 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.23919/SpliTech52315.2021.9566351" target="_blank" >http://dx.doi.org/10.23919/SpliTech52315.2021.9566351</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/SpliTech52315.2021.9566351" target="_blank" >10.23919/SpliTech52315.2021.9566351</a>

Result language
angličtina
Original language name
Plastic waste categorisation using machine learning methods-metals contaminations
Original language description
The global plastic consumption is consistently increasing, and plastic recycling is still a crucial issue to be solved due to the depletion of fossil resources. The polymers in the plastic can be chemically enhanced with materials such as colourants or metal fillers. This work aims to analyse the metal contamination data in the virgin plastic and plastic waste to derive a general categorisation rule for different plastic polymers (PET, PE, PP, PS). The metals contamination in plastics can be accumulated during use or waste management practices during recycling, which can be harmful for application. The metal concentrations for plastic streams are sampled from different origins: virgin plastic, household waste, and reprocessed household and industrial waste. Machine Learning methods, specifically the tree-based classification models, are used to derive a series of 'if-then' rules for classifying the plastic waste based on the sampled data. This helps the identification of the data patterns on the plastic streams and aids in deriving a categorisation rule for any plastic. The data-driven plastic categorisation could help in understanding the current waste practices and determining a proper recycling plan for local or even global plastic waste. © 2021 University of Split, FESB.
Czech name
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Czech description
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Project
<a href="/en/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Sustainable Process Integration Laboratory (SPIL)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)
ISBN
9789532901122
ISSN
—
e-ISSN
—
Number of pages
13
Pages from-to
173101-173101
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
neuveden
Event location
Bol and Split
Event date
Sep 8, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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