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

doi:10.1016/j.heliyon.2023.e21041

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

Heliyon. 2023 Oct 19;9(11):e21041. doi: 10.1016/j.heliyon.2023.e21041. eCollection 2023 Nov.

Authors

Affiliations

¹ Sustainability Solutions Research Lab, University of Pannonia, Egyetem str. 10, H-8200 Veszprem, Hungary.
² Department of Chemical and Petroleum Refining Engineering, College of Oil and Gas Engineering, Basra University for Oil and Gas, Iraq.
³ Nanotechnology and advanced material research center, University of Technology- Iraq.
⁴ Chemical Engineering Department, College of Engineering, Al-Muthanna University, Al-Muthanna, Iraq.
⁵ The University of Danang,University of Science and Technology, Danang 550000, Viet Nam.
⁶ Chemical Engineering department, University of Technology, Baghdad 10070, Iraq.
⁷ Institute of Radiochemistry and Radioecology, Research Centre for Biochemical, Environmental and Chemical Engineering, University of Pannonia, 8200 Veszprem, Hungary.
⁸ Department of Materials Engineering, Faculty of Engineering, University of Pannonia, 8201 Veszprém, Hungary.
⁹ Department of Organic Chemistry, Institute of Environmental Engineering, University of Pannonia, H-8201 Veszprém, P. O. Box 158, Hungary.
¹⁰ Mechanical Engineering Department, University of Technology - Iraq, Baghdad, Iraq.

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^-6 and 6.1387x10^-5 for C 2.2.1 and C 2.1.1 respectively. The optimized FFNN architecture of 4-10-1 was constructed for modeling recovery of Sc. The extraction process was well modeled by the FFNN with an R² of 0.999 for the two MC, indicating that the observed Sc recovery efficiency consistent with the predicted one.

Keywords: Artificial neural network; Macrocyclic compounds; Molecular recognition; NARX model; Scandium.