Exploring Optimization of Zeolites as Adsorbents for Rare Earth Elements in Continuous Flow by Machine Learning Techniques

Óscar Barros; Pier Parpot; Isabel C Neves; Teresa Tavares

doi:10.3390/molecules28247964

Exploring Optimization of Zeolites as Adsorbents for Rare Earth Elements in Continuous Flow by Machine Learning Techniques

Molecules. 2023 Dec 6;28(24):7964. doi: 10.3390/molecules28247964.

Authors

Óscar Barros^{1

2}, Pier Parpot^{1

2}, Isabel C Neves^{1

2}, Teresa Tavares^{1

3}

Affiliations

¹ CEB-Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
² CQUM, Centre of Chemistry, Chemistry Department, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
³ LABBELS-Associate Laboratory, 4710-057 Braga, Portugal.

Abstract

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.

Keywords: circular economy; machine learning; rare earth elements; sorption processes; zeolites.

Abstract

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