Data-Driven Recyclability Classification of Plastic Waste

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU143063" target="_blank" >RIV/00216305:26210/21:PU143063 - isvavai.cz</a>

Result on the web

<a href="http://www.cetjournal.it/cet/21/88/113.pdf" target="_blank" >http://www.cetjournal.it/cet/21/88/113.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3303/CET2188113" target="_blank" >10.3303/CET2188113</a>

Result language

angličtina

Original language name

Data-Driven Recyclability Classification of Plastic Waste

Original language description

This work aims to propose a general data-driven plastic waste categorisation procedure that defines their recyclability based on classification into material recycling classes. The contamination in plastics, such as metal fillers or additives, is accumulated during the entire Life Cycle, which can be harmful to either mechanical or chemical recycling. The plastic polymers can also degrade during recycling due to weakened chemical bonds in the polymers. The diversity of plastic material types and products makes it necessary to use a data-driven quality-based definition of plastic waste properties to facilitate proper waste recycling and mitigation. This study demonstrates the use of Machine Learning tools that enable automated classification to analyse the plastic waste data and derive the indicators for plastic waste recyclability. Tree-based models such as the Decision Tree Model and Random Forest Algorithm are used as they produce interpretable if-then rules for plastic waste categorisation. The proposed method allows an analysis of the metal contamination and degradation data in a collection of plastic material samples or batches to derive a general categorisation rule for a polymer type – PE. 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 Italian Association of Chemical Engineering - AIDIC. All rights reserved.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

20704 - Energy and fuels

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

Name of the periodical

Chemical Engineering Transactions

ISSN

2283-9216

e-ISSN

—

Volume of the periodical

neuveden

Issue of the periodical within the volume

88

Country of publishing house

IT - ITALY

Number of pages

6

Pages from-to

679-684

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85122526987