Plastic waste categorisation using machine learning methods-metals contaminations

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Plastic waste categorisation using machine learning methods-metals contaminations

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Plastic waste categorisation using machine learning methods-metals contaminations

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

<a href="/cs/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Laboratoř integrace procesů pro trvalou udržitelnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)

ISBN

9789532901122

ISSN

—

e-ISSN

—

Počet stran výsledku

13

Strana od-do

173101-173101

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

neuveden

Místo konání akce

Bol and Split

Datum konání akce

8. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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