Evidence map›Paper›PMID 37928005›Full record

ArticleHeliyon2023

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.

Ali Dawood Salman, Saja Mohsen Alardhi, Forat Yasir AlJaberi, Moayyed G Jalhoom, Phuoc-Cuong Le, Shurooq Talib Al-Humairi, Mohammademad Adelikhah, Miklós Jakab, Gergely Farkas, Alaa Abdulhady Jaber

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In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Ali Dawood SalmanSustainability Solutions Research Lab, University of Pannonia, Egyetem str. 10, H-8200 Veszprem, Hungary.
Saja Mohsen AlardhiNanotechnology and advanced material research center, University of Technology- Iraq.
Forat Yasir AlJaberiChemical Engineering Department, College of Engineering, Al-Muthanna University, Al-Muthanna, Iraq.
Moayyed G JalhoomNanotechnology and advanced material research center, University of Technology- Iraq.
Phuoc-Cuong LeThe University of Danang,University of Science and Technology, Danang 550000, Viet Nam.
Shurooq Talib Al-HumairiChemical Engineering department, University of Technology, Baghdad 10070, Iraq.
Mohammademad AdelikhahInstitute of Radiochemistry and Radioecology, Research Centre for Biochemical, Environmental and Chemical Engineering, University of Pannonia, 8200 Veszprem, Hungary.
Miklós JakabDepartment of Materials Engineering, Faculty of Engineering, University of Pannonia, 8201 Veszprém, Hungary.
Gergely FarkasDepartment of Organic Chemistry, Institute of Environmental Engineering, University of Pannonia, H-8201 Veszprém, P. O. Box 158, Hungary.
Alaa Abdulhady JaberMechanical Engineering Department, University of Technology - Iraq, Baghdad, Iraq.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

Indexed as

Artificial neural networkMacrocyclic compoundsMolecular recognitionNARX modelScandium

Identifiers

PMID37928005
PMCPMC10623173

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.