Critical rare earth metal adsorption onto expanded vermiculite: accurate modeling through response surface methodology and machine learning techniques

dc.creatorde Vargas Brião, Giani
dc.creatorPfingsten Franco, Dison Stracke
dc.creatorVasconcelos da Silva, Flávio
dc.creatorCarlos da Silva, Meuris Gurgel
dc.creatorGurgel Adeodato Vieira, Melissa
dc.date2023-02-28T16:49:10Z
dc.date2025
dc.date2023-02-28T16:49:10Z
dc.date2023
dc.date.accessioned2023-10-03T20:08:09Z
dc.date.available2023-10-03T20:08:09Z
dc.identifierGiani de Vargas Brião, Dison Stracke Pfingsten Franco, Flávio Vasconcelos da Silva, Meuris Gurgel Carlos da Silva, Melissa Gurgel Adeodato Vieira, Critical rare earth metal adsorption onto expanded vermiculite: Accurate modeling through response surface methodology and machine learning techniques, Sustainable Chemistry and Pharmacy, Volume 31, 2023, 100938, ISSN 2352-5541, https://doi.org/10.1016/j.scp.2022.100938.
dc.identifierhttps://hdl.handle.net/11323/9935
dc.identifier10.1016/j.scp.2022.10093
dc.identifier2352-5541
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9174438
dc.descriptionThe circular economy of rare earth metals (REM), such as dysprosium, is essential for a sustainable future in clean energy and high-tech fields. Adsorption has gained attention to recover and reintegrate REM into the productive chain; however, accurate modeling of adsorptive processes still needs to be addressed, which delays further scale-up studies. Thus, this paper studied the adsorption of Dy on expanded vermiculite and applied novel empirical methods, such as artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS), and the classical response surface methodology (RSM) for modeling of dysprosium recovery as a function of adsorbent size, mass, and pH of the solution, variables often ignored in the mathematical modeling of adsorption. In our work, the effect of each operational parameter on Dy removal efficiency was examined by the RSM approach, in which only the adsorbent dosage and pH were the significant factors. So, ANFIS with two input membership functions (Gaussian type) was the most accurate procedure to predict and model the dysprosium removal efficiency (R2 = 0.99681). Also, vermiculite removed dysprosium with high efficiency (99.2%) under the best experimental conditions, at pH around 3.5 and adsorbent mass of 0.64 g, indicating an effective process optimization. The loaded vermiculite, after the adsorption, was characterized by textural and thermal properties that confirmed the stability of the material. Thus, according to basic operational parameters, accurate modeling of the Dy adsorption on expanded vermiculite (a low-cost, available, and non-toxic material) can improve adsorption technology's maturity to reconcentrate and recover rare earth metals.
dc.format1 página
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherElsevier BV
dc.publisherNetherlands
dc.relationSustainable Chemistry and Pharmacy
dc.relation31
dc.rights© 2022 Elsevier B.V. All rights reserved.
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rightshttp://purl.org/coar/access_right/c_f1cf
dc.sourcehttps://www.sciencedirect.com/science/article/abs/pii/S2352554122003424
dc.subjectDysprosium
dc.subjectAdsorption
dc.subjectVermiculite
dc.subjectRSM
dc.subjectMachine learning
dc.titleCritical rare earth metal adsorption onto expanded vermiculite: accurate modeling through response surface methodology and machine learning techniques
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_2df8fbb1
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/draft
dc.typehttp://purl.org/coar/version/c_b1a7d7d4d402bcce


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