Exploring optimization of zeolites as adsorbents for rare earth elements in continuous flow by machine learning techniques

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. O.B. thanks FCT for the concession of his Ph.D. grant (SFRH/BD/140362/2018). This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020, UIDP/04469/2020, LA/P/0029/2020 and UID/QUI/0686/2020 units and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020—Programa Operacional Regional do Norte, Portugal.