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Browsing by Author "Costa, Samuel Felipe Martins"

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  • Green solvents and AI-driven models for recycling 3D printing waste
    Publication . Costa, Samuel Felipe Martins; Abranches , Dinis Oliveira; Ferreira , Olga; Patrício, Patrícia Santiago de Oliveira
    The increasing adoption of 3D printing, particularly using polylactic acid (PLA), has led to a significant rise in plastic waste, calling for the development of sustainable recycling solutions. Among recycling approaches, the physical method is gaining more space, especially with advances in the use of green solvents. Therefore, this study examines the application of green solvents and artificial intelligence (AI)-driven models for the dissolution and recovery of PLA from 3D printing waste. In particular, the research focuses on identifying environmentally friendly solvents, based on qualitative PLA dissolution data, using machine learning (ML) techniques to find and predict the best solvents for dissolving PLA while minimizing contamination from additives and other polymers. Among the solvents initially investigated, dimethylformamide (DMF), chloroform (CLFM), dimethyl carbonate (DC), and isosorbide dimethyl (IDE) achieved complete dissolution of PLA after 24 h at 50 °C. Dissolution behavior was further examined above and below the PLA glass transition temperature (Tg = 55 - 60 °C), with only ethyl acetate (EtAce) changing from a poor solvent to a good solvent with increasing temperature. The Hansen Solubility Parameters (HSP) and the infinite dilution activity coefficients (γ∞) predicted by COSMO-RS were employed to rationalize the dissolution behavior, showing unsatisfactory discrimination between good and poor solvents. Subsequently, ML models were applied to the experimental dataset to identify additional suitable solvents. The results demonstrated excellent predictive performance, correctly classifying good and poor solvents for PLA and identifying new good solvents as acetonitrile (ACN), methyl acetate (MeAce), and dichloromethane (DCM). Overall, by integrating solvent-based recycling with AI-driven optimization, this work showed potential solvents to enhance the circular economy of PLA-based materials, promoting more sustainable and effective waste management practices.
    2025Master thesis Restricted access Show more