Publication detail

Plastic Circular Economy Framework using Hybrid Machine Learning and Pinch Analysis

Chin, H.H., Varbanov, P.S., You, F., Sher, F., Klemeš, J.J.

Original Title

Plastic Circular Economy Framework using Hybrid Machine Learning and Pinch Analysis

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

Machine Learning; Pinch Analysis; Plastic Circular Economy; Plastic recycling

Authors

Chin, H.H., Varbanov, P.S., You, F., Sher, F., Klemeš, J.J.

Released

1. 9. 2022

Publisher

Elsevier B.V.

ISBN

0921-3449

Periodical

RESOURCES CONSERVATION AND RECYCLING

Number

184

State

Kingdom of the Netherlands

Pages from

106387

Pages to

106387

Pages count

14

URL

https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0921344922002312

BibTex

@article{BUT178089,
  author="Hon Huin {Chin} and Petar Sabev {Varbanov} and Jiří {Klemeš}",
  title="Plastic Circular Economy Framework using Hybrid Machine Learning and Pinch Analysis",
  journal="RESOURCES CONSERVATION AND RECYCLING",
  year="2022",
  number="184",
  pages="106387--106387",
  doi="10.1016/j.resconrec.2022.106387",
  issn="0921-3449",
  url="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S0921344922002312"
}