Data-Driven Recyclability Classification of Plastic Waste

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 b...

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Autores principales:	Hon Huin Chin, Petar Sabev Varbanov, Dániel Fózer, Péter Mizsey, Jirí Jaromír Klemeš, Xuexiu Jia
Formato:	article
Lenguaje:	EN
Publicado:	AIDIC Servizi S.r.l. 2021
Materias:	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
Acceso en línea:	https://doaj.org/article/574c0f183f16413d88512ffaa56bc653
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spelling	oai:doaj.org-article:574c0f183f16413d88512ffaa56bc6532021-11-15T21:47:53ZData-Driven Recyclability Classification of Plastic Waste10.3303/CET21881132283-9216https://doaj.org/article/574c0f183f16413d88512ffaa56bc6532021-11-01T00:00:00Zhttps://www.cetjournal.it/index.php/cet/article/view/11906https://doaj.org/toc/2283-9216This 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.Hon Huin Chin Petar Sabev VarbanovDániel FózerPéter MizseyJirí Jaromír KlemešXuexiu JiaAIDIC Servizi S.r.l.articleChemical engineeringTP155-156Computer engineering. Computer hardwareTK7885-7895ENChemical Engineering Transactions, Vol 88 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
spellingShingle	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895 Hon Huin Chin Petar Sabev Varbanov Dániel Fózer Péter Mizsey Jirí Jaromír Klemeš Xuexiu Jia Data-Driven Recyclability Classification of Plastic Waste
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.
format	article
author	Hon Huin Chin Petar Sabev Varbanov Dániel Fózer Péter Mizsey Jirí Jaromír Klemeš Xuexiu Jia
author_facet	Hon Huin Chin Petar Sabev Varbanov Dániel Fózer Péter Mizsey Jirí Jaromír Klemeš Xuexiu Jia
author_sort	Hon Huin Chin
title	Data-Driven Recyclability Classification of Plastic Waste
title_short	Data-Driven Recyclability Classification of Plastic Waste
title_full	Data-Driven Recyclability Classification of Plastic Waste
title_fullStr	Data-Driven Recyclability Classification of Plastic Waste
title_full_unstemmed	Data-Driven Recyclability Classification of Plastic Waste
title_sort	data-driven recyclability classification of plastic waste
publisher	AIDIC Servizi S.r.l.
publishDate	2021
url	https://doaj.org/article/574c0f183f16413d88512ffaa56bc653
work_keys_str_mv	AT honhuinchin datadrivenrecyclabilityclassificationofplasticwaste AT petarsabevvarbanov datadrivenrecyclabilityclassificationofplasticwaste AT danielfozer datadrivenrecyclabilityclassificationofplasticwaste AT petermizsey datadrivenrecyclabilityclassificationofplasticwaste AT jirijaromirklemes datadrivenrecyclabilityclassificationofplasticwaste AT xuexiujia datadrivenrecyclabilityclassificationofplasticwaste
_version_	1718426787370762240

Data-Driven Recyclability Classification of Plastic Waste

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