Defining the optimal conditions using FFNNs and NARX neural networks for modelling the extraction of Sc from aqueous solution by Cryptand-2.2.1 and Cryptand-2.1.1.
Artificial neural network
Macrocyclic compounds
Molecular recognition
NARX model
Scandium
Journal
Heliyon
ISSN: 2405-8440
Titre abrégé: Heliyon
Pays: England
ID NLM: 101672560
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
08
05
2023
revised:
10
10
2023
accepted:
13
10
2023
medline:
6
11
2023
pubmed:
6
11
2023
entrez:
6
11
2023
Statut:
epublish
Résumé
The main aim of this study is to figure out how well cryptand-2.2.1 (C 2.2.1) and cryptand-2.1.1 (C 2.1.1) macrocyclic compounds (MCs) work as novel extractants for scandium (Sc) by using an artificial neural network (ANN) models in MATLAB software. Moreover, C2.2.1 and C2.1.1 have never been evaluated to recover Sc. The independent variables impacting the extraction process (concentration of MC, concentration of Sc, pH, and time), and a nonlinear autoregressive network with exogenous input (NARX) and feed-forward neural network (FFNN) models were used to estimate their optimum values. The greatest obstacle in the selective recovery process of the REEs is the similarity in their physicochemical properties, specifically their ionic radius. The recovery of Sc from the aqueous solution was experimentally evaluated, then the non-linear relationship between those parameters was predictively modeled using (NARX) and (FFNN). To confirm the extraction and stripping efficiency, an atomic absorption spectrophotometer (AAS) was employed. The results of the extraction investigations show that, for the best conditions of 0.008 mol/L MC concentration, 10 min of contact time, pH 2 of the aqueous solution, and 75 mg/L Sc initial concentration, respectively, the C 2.1.1 and C 2.2.1 extractants may reach 99 % of Sc extraction efficiency. Sc was recovered from a multi-element solution of scandium (Sc), yttrium (Y), and lanthanum (La) under these circumstances. Whereas, at a concentration of 0.3 mol/L of hydrochloric acid, the extraction of Sc was 99 %, as opposed to Y 10 % and La 7 %. The Levenberg-Marquardt training algorithm had the best training performance with an mean-squared-error, MSE, of 5.232x10
Identifiants
pubmed: 37928005
doi: 10.1016/j.heliyon.2023.e21041
pii: S2405-8440(23)08249-X
pmc: PMC10623173
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e21041Informations de copyright
© 2023 The Authors. Published by Elsevier Ltd.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Références
Environ Sci Pollut Res Int. 2021 Jun;28(22):28428-28443
pubmed: 33538976
Anal Chem. 2004 May 15;76(10):2773-9
pubmed: 15144187
Materials (Basel). 2022 Mar 23;15(7):
pubmed: 35407709
Molecules. 2021 Dec 13;26(24):
pubmed: 34946635
J Mol Model. 2007 Sep;13(9):1017-25
pubmed: 17632741
Chemosphere. 2020 Oct;256:127081
pubmed: 32447112
Materials (Basel). 2020 Dec 15;13(24):
pubmed: 33334052
Chem Rev. 2009 Oct;109(10):4921-60
pubmed: 19715312
Chemosphere. 2017 May;175:365-372
pubmed: 28236706
Eur J Gastroenterol Hepatol. 2007 Dec;19(12):1046-54
pubmed: 17998827
Inorg Chem. 2021 May 3;60(9):6125-6134
pubmed: 33866779
Angew Chem Int Ed Engl. 2008;47(16):3026-8
pubmed: 18327860
Heliyon. 2023 Apr 17;9(4):e15455
pubmed: 37128319
Chemistry. 2009 Oct 26;15(42):11346-60
pubmed: 19790212
Food Chem. 2022 Jan 1;366:130689
pubmed: 34343950
Heliyon. 2023 Jul 04;9(7):e17794
pubmed: 37456018
Int J Mol Sci. 2019 Aug 28;20(17):
pubmed: 31466219
Heliyon. 2023 Jan 10;9(1):e12888
pubmed: 36699265
Environ Pollut. 2022 Sep 1;308:119596
pubmed: 35716890