Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/88501
Título: | Exploring optimization of zeolites as adsorbents for rare earth elements in continuous flow by machine learning techniques |
Autor(es): | Barros, Óscar Parpot, Pier Neves, Isabel C. Tavares, Teresa |
Palavras-chave: | rare earth elements zeolites machine learning sorption processes circular economy |
Data: | 2023 |
Editora: | MDPI |
Revista: | Molecules |
Citação: | Barros, Ó.; Parpot, P.; Neves, I.C.; Tavares, T. Exploring Optimization of Zeolites as Adsorbents for Rare Earth Elements in Continuous Flow by Machine Learning Techniques. Molecules 2023, 28, 7964. https://doi.org/10.3390/molecules28247964 |
Resumo(s): | Unsupervised machine learning (ML) techniques are applied to the characterization of the adsorption of rare earth elements (REEs) by zeolites in continuous flow. The successful application of principal component analysis (PCA) and K-Means algorithms from ML allowed for a wide range assessment of the adsorption results. This global approach permits the evaluation of the different stages of the sorption cycles and their optimization and improvement. The results from ML are also used for the definition of a regression model to estimate other REEs’ recoveries based on the known values of the tested REEs. Overall, it was possible to remove more than 70% of all REEs from aqueous solutions during the adsorption assays and to recover over 80% of the REEs entrapped on the zeolites using an optimized desorption cycle. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/88501 |
DOI: | 10.3390/molecules28247964 |
e-ISSN: | 1420-3049 |
Versão da editora: | https://www.mdpi.com/1420-3049/28/24/7964 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series CDQuim - Artigos (Papers) |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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document_56572_1.pdf | 4,12 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons