Machine learning constructs the microstructure and mechanical properties that accelerate the development of CFRP pyrolysis for carbon-fiber recycling
Copyright © 2024 Elsevier Ltd. All rights reserved.
| Veröffentlicht in: | Waste management (New York, N.Y.). - 1999. - 190(2024) vom: 10. Sept., Seite 12-23 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | Waste management (New York, N.Y.) |
| Schlagworte: | Journal Article Machine learning Pyrolysis Recycled carbon fiber Recycling |
| Zusammenfassung: | Copyright © 2024 Elsevier Ltd. All rights reserved. The increasing use of carbon-fiber-reinforced plastic (CFRP) has led to its post-end-of-life recycling becoming a research focus. Herein, we studied the macroscopic and microscopic characteristics of recycled carbon fiber (rCF) during CFRP pyrolysis by innovatively combining typical experiments with machine learning. We first comprehensively studied the effects of treatment time and temperature on the mechanical properties, graphitization degree, lattice parameters, and surface O content of rCF following pyrolysis and oxidation. The surface resin residue was found to largely affect the degradation of the mechanical properties of the rCF, whereas oxidation treatment effectively removes this residue and is the critical recycling condition that determines its mechanical properties. In contrast, pyrolysis affected graphitization in a more-pronounced manner. More importantly, a random forest machine-learning model (RF model) that optimizes using a particle swarm algorithm was developed based on 336 data points and used to determine the mechanical properties and microstructural parameters of rCF when treated under various pyrolysis and oxidation conditions. The constructed model was effectively used to forecast the recovery conditions for various rCF target requirements, with the predictions for different recycling conditions found to be in good agreement with the experimental data |
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| Beschreibung: | Date Revised 11.09.2024 published: Print-Electronic Citation Status Publisher |
| ISSN: | 1879-2456 |
| DOI: | 10.1016/j.wasman.2024.09.002 |