Solvent extraction of titanium from ilmenite hydrochloric acid leachate: Optimization and investigation of extraction reactions of all contained metal ions

Zhu, Keyu; Wei, Qifeng; Li, Huaquan; Ren, Xiulian

doi:10.1016/j.mineng.2022.107744

Cited by 8 publications

(1 citation statement)

References 35 publications

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“…Solvent extraction (SE) has been widely used for heavy metal recovery [1][2][3] from aqueous solutions, particularly the extraction of noble and rare metals by using different extracting solvents. There are several applications of metal solvent extraction in the literature, such as extraction of Ti, Fe, Zn, Cu, Mn, Co, Ni, Li, Sc, Al, Ca, Ta, Nb, and U [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Optimization and Prediction of Stability of Emulsified Liquid Membrane (ELM): Artificial Neural Network

Zamouche¹,

Tahraoui

Laggoun³

et al. 2023

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In this work, the emulsified liquid membrane (ELM) extraction process was studied as a technique for separating different pollutants from an aqueous solution. The emulsified liquid membrane used consisted of Sorbitan mono-oleate (Span 80) as a surfactant with n-hexane (C6H14) as a diluent; the internal phase used was nitric acid (HNO3). The major constraint in the implementation of the extraction process by an emulsified liquid membrane (ELM) is the stability of the emulsion. However, this study focused first on controlling the stability of the emulsion by optimizing many operational factors, which have a direct impact on the stability of the membrane. Among the important parameters that cause membrane breakage, the surfactant concentration, the emulsification time, and the stirring speed were demonstrated. The optimization results obtained showed that the rupture rate (Tr) decreased until reaching a minimum value of 0.07% at 2% of weight/weight of Span 80 concentration with an emulsification time of 3 min and a stirring speed of 250 rpm. On the other hand, the volume of the inner phase leaking into the outer phase was predicted using an artificial neural network (ANN). The evaluation criteria of the ANN model in terms of statistical coefficient and RMSE error revealed very interesting results and the performance of the model since the statistical coefficients were very high and close to 1 in the four phases (R_training = 0.99724; R_validation = 0.99802; R_test = 0.99852; R_all data = 0.99772), and also, statistical errors of RMSE were minimal (RMSE_training= 0.0378; RMSE_validation = 0.0420; RMSE_test = 0.0509; RMSE_all data = 0.0406).

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