Plastic Circular Economy Framework using Hybrid Machine Learning and Pinch Analysis

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Plastic Circular Economy Framework using Hybrid Machine Learning and Pinch Analysis
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20704 - Energy and fuels

Project
<a href="/en/project/GF21-45726L" target="_blank" >GF21-45726L: Sustainable Plastic Value Chain to Support a Circular Economy Transition</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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