期刊
RESOURCES CONSERVATION AND RECYCLING
卷 184, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.resconrec.2022.106387
关键词
Plastic recycling; Plastic Circular Economy; Machine Learning; Pinch Analysis
资金
- Grant Agency of the Czech Republic [21-45726L]
- Ministry of Education, Science and Sport of the Republic of Slovenia [5442-1/2018/106]
- Slovenian Research Agency [P2-0412, P2-0421, J7-3149]
This study utilizes machine learning approach to evaluate the recyclability of plastic waste by categorizing the quality trends of polymers, aiming to maximize the waste quality grading system and resource utilization.
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.
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