Data-Driven Recyclability Classification of Plastic Waste

Kód výsledku v IS VaVaI

RIV/00216305:26210/21:PU143063 - isvavai.cz

Výsledek na webu

http://www.cetjournal.it/cet/21/88/113.pdf

DOI - Digital Object Identifier

10.3303/CET2188113

Jazyk výsledku

angličtina

Název v původním jazyce

Data-Driven Recyclability Classification of Plastic Waste

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Data-Driven Recyclability Classification of Plastic Waste

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

EF15_003/0000456: Laboratoř integrace procesů pro trvalou udržitelnost

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 periodika

Chemical Engineering Transactions

ISSN

2283-9216

e-ISSN

—

Svazek periodika

neuveden

Číslo periodika v rámci svazku

88

Stát vydavatele periodika

IT - Italská republika

Počet stran výsledku

6

Strana od-do

679-684

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85122526987