Dawood Salman Ali, Alardhi Saja Mohsen, AlJaberi Forat Yasir, Jalhoom Moayyed G, Le Phuoc-Cuong, Al-Humairi Shurooq Talib, Adelikhah Mohammademad, Farkas Gergely, Abdulhady Jaber Alaa
Sustainability Solutions Research Lab, University of Pannonia, Egyetem str. 10, H-8200 Veszprem, Hungary.
Department of Chemical and Petroleum Refining Engineering, College of Oil and Gas Engineering, Basra University for Oil and Gas, Iraq.
Heliyon. 2023 Oct 19;9(11):e21041. doi: 10.1016/j.heliyon.2023.e21041. eCollection 2023 Nov.
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 and 6.1387x10 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 R of 0.999 for the two MC, indicating that the observed Sc recovery efficiency consistent with the predicted one.
本研究的主要目的是通过使用MATLAB软件中的人工神经网络（ANN）模型，弄清楚穴状配体-2.2.1（C 2.2.1）和穴状配体-2.1.1（C 2.1.1）大环化合物（MCs）作为钪（Sc）新型萃取剂的效果如何。此外，C2.2.1和C2.1.1从未被评估用于回收Sc。影响萃取过程的自变量（MC浓度、Sc浓度、pH值和时间），以及具有外部输入的非线性自回归网络（NARX）和前馈神经网络（FFNN）模型被用于估计它们的最佳值。稀土元素（REEs）选择性回收过程中的最大障碍是它们物理化学性质的相似性，特别是它们的离子半径。通过实验评估了从水溶液中回收Sc的情况，然后使用（NARX）和（FFNN）对这些参数之间的非线性关系进行了预测建模。为了确认萃取和反萃效率，使用了原子吸收分光光度计（AAS）。萃取研究结果表明，在MC浓度为0.008 mol/L、接触时间为10分钟、水溶液pH值为2、Sc初始浓度为75 mg/L的最佳条件下，C 2.1.1和C 2.2.1萃取剂的Sc萃取效率可达到99%。在这些情况下，从钪（Sc）、钇（Y）和镧（La）的多元素溶液中回收了Sc。而在盐酸浓度为0.3 mol/L时，Sc的萃取率为99%，相比之下，Y为10%，La为7%。Levenberg-Marquardt训练算法具有最佳的训练性能，C 2.2.1和C 2.1.1的均方误差（MSE）分别为5.232x10和6.1387x10。构建了4-10-1的优化FFNN架构用于模拟Sc的回收。FFNN对两种MC的萃取过程进行了很好的建模，R值为0.999，表明观察到的Sc回收效率与预测值一致。
