Plastic Circular Economy Framework using Hybrid Machine Learning and Pinch Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F22%3APU144664" target="_blank" >RIV/00216305:26210/22:PU144664 - isvavai.cz</a>

Výsledek na webu

<a href="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0921344922002312" target="_blank" >https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0921344922002312</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.resconrec.2022.106387" target="_blank" >10.1016/j.resconrec.2022.106387</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Plastic Circular Economy Framework using Hybrid Machine Learning and Pinch Analysis

Popis výsledku v původním jazyce

The worldwide plastic waste accumulation has posed probably irreversible harm to the environment, and the main dilemma for this global issue is: How to define the waste quality grading system to maximise plastic recyclability? This work reports a machine learning approach to evaluating the recyclability of plastic waste by categorising the quality trends of the contained polymers with auxiliary materials. The result reveals the hierarchical resource quality grades predictors that restrict the mapping of the waste sources to the demands. The Pinch Analysis framework is then applied using the quality clusters to maximise plastic recyclability. The method identifies a Pinch Point – the ideal waste quality level that limits the plastic recycling rate in the system. The novel concept is applied to a problem with different polymer types and properties. The results show the maximum recycling rate for the case study to be 38 % for PET, 100 % for PE and 92 % for PP based on the optimal number of clusters identified. Trends of environmental impacts with different plastic recyclability and footprints of recycled plastic are also compared.

Název v anglickém jazyce

Plastic Circular Economy Framework using Hybrid Machine Learning and Pinch Analysis

Popis výsledku anglicky

The worldwide plastic waste accumulation has posed probably irreversible harm to the environment, and the main dilemma for this global issue is: How to define the waste quality grading system to maximise plastic recyclability? This work reports a machine learning approach to evaluating the recyclability of plastic waste by categorising the quality trends of the contained polymers with auxiliary materials. The result reveals the hierarchical resource quality grades predictors that restrict the mapping of the waste sources to the demands. The Pinch Analysis framework is then applied using the quality clusters to maximise plastic recyclability. The method identifies a Pinch Point – the ideal waste quality level that limits the plastic recycling rate in the system. The novel concept is applied to a problem with different polymer types and properties. The results show the maximum recycling rate for the case study to be 38 % for PET, 100 % for PE and 92 % for PP based on the optimal number of clusters identified. Trends of environmental impacts with different plastic recyclability and footprints of recycled plastic are also compared.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

<a href="/cs/project/GF21-45726L" target="_blank" >GF21-45726L: Udržitelný hodnotový řetězec plastů pro přechod na oběhové hospodářství</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

RESOURCES CONSERVATION AND RECYCLING

ISSN

0921-3449

e-ISSN

1879-0658

Svazek periodika

neuveden

Číslo periodika v rámci svazku

184

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

106387-106387

Kód UT WoS článku

000798121400004

EID výsledku v databázi Scopus

2-s2.0-85129641594