Supported liquid membranes (SLM) for recovery of bismuth from aqueous solutions

In this study, the extraction of Bi(III) from synthetic solutions of 2 M H₂SO₄/0.5 M HCl by supported liquid membranes (SLM) using tri-n-octylphosphine oxide (Cyanex 921) as extractant is reported. First, the nature of the Bi(III)/Cyanex 921 solvates extracted to organic phase (in a solvent extraction system) was determined by the slope method. It was found that Bi(III) reacts with 2 molecules of Cyanex 921 to form the solvate BiCl₃·2Cyanex 921. In the recovery of Bi(III) by the SLM system, parameters that influence extraction efficiency were evaluated, including: support, feed solution and stripping solution nature, and extractant concentration in the organic phase which impregnates the support. Results indicate that Cyanex 921 dissolved in kerosene is not able to extract Bi(III) from H₂SO₄ media. Moreover, transfer of H₂SO₄ was observed. HCl addition to the feed solution up to a maximum concentration of 0.5 M increases Bi(III) extraction. Further increase in HCl concentration causes a decrease in Bi(III) transfer. Likewise, the concentration of Cyanex 921 in the SLM organic phase which produced the maximum Bi(III) extraction was found to be 0.3 M. The performance of H₂O and 0.2 M H₂SO₄ as stripping solutions was evaluated, and it was found that only H₂SO₄ enabled Bi(III) transfer.     

Liquid-liquid extraction of tetravalent zirconium from acidic chloride solutions using Cyanex 272.

The liquid-liquid extraction of zirconium(IV) from acidic chloride solutions was carried out with Cyanex 272 as an extractant diluted in kerosene. An increase of the acid concentration decreased the percentage extraction of metal, which indicates that the extraction follows ion exchange-type mechanism: MO2+(aq) + 2(HA)2(org) <--> MO (HA2)2(org) + 2H+(aq), where, M = Zr(IV); HA = Cyanex 272. The extraction of Zr(IV) increases with an increase of the extractant concentration. In a plot of log D vs. log[extractant], M is linear with a slope of approximately 2, indicating the association of two moles of extractant with the extracted metal species. On the other hand, the extraction decreases with an increase of the H+ ion concentration. A plot of log D vs. log[H+] gave a straight line with a negative slope of 1.7, indicating the exchange of two moles of hydrogen ions for every mole of Zr(IV). The effect of the Cl- ion concentration at a constant concentration of [H+] did not show any change in the D values. The addition of sodium salts enhanced the percentage extraction of metal, and followed the order of NaSCN > NaNO3 > Na2SO4 > NaCl. The stripping of metal from the loaded organic (L.O) with different acids indicated sulfuric acid to be the best stripping agent. An increase of the temperature during the extraction and stripping stages increases the metal transfer, showing that the process is exothermic. The synergism, regeneration and recycling capacity of Cyanex 272; the extraction behavior of associated elements, such as Hf(IV), Ti(IV), Al(III), Fe(III); and IR spectra of the extracted Zr-Cyanex 272 complex were studied.     

Separation of Zr and Hf from strong hydrochloric acid solution by solvent extraction with TEHA

In order to separate Zr(IV) and Hf(IV) from chloride solutions, TEHA (tri 2-ethyl hexyl amine) was used as an extractant. The aqueous phase consisted of 200 ppm of Zr and Hf in strong HCl solution. In our solvent extraction system, the extractability of the constituents by TEHA was found to be in the following order, HCl > Zr(IV) > Hf(IV). The highest separation factor between the two metals was obtained from 8 M HCl solution. Based on the selectivity towards Zr over Hf with TEHA, McCabe–Thiele plot was constructed and batch simulation of counter-current extraction studies has been conducted. Scrubbing results from the loaded TEHA showed that Hf was selectively scrubbed over Zr by strong HCl solution (9 M). Complete stripping of Zr was possible from the organic phase with distilled water after scrubbing of Hf. The extraction behavior of Zr and Hf by TEHA was compared with that by TiOA and TOA. Our results can be utilized in developing a solvent extraction process to separate Zr and Hf from concentrated chloride solutions by using TEHA.     

Extraction of neodymium(III) using binary mixture of Cyanex 272 and Cyanex 921/Cyanex 923 in kerosene

The extraction of Nd(III) using binary mixtures of Cyanex 272 (HA), Cyanex 921/Cyanex 923 (B) in kerosene from nitric acid medium has been investigated. The effect of aqueous phase acidity, extractant concentration, nitrate ion concentration and diluents on the extraction of Nd(III) has been studied. On the basis of slope analysis results, extracted species are proposed as Nd(NO₃)A₂·3HA and Nd(NO₃)₂·A·3HA·B using Cyanex 272 and its mixture with Cyanex 921/Cyanex 923, respectively. With the mixture of 0.1 M Cyanex 272 and 0.1 M Cyanex 923 in kerosene, the extraction of 0.001 M Nd(III) from 0.001 M HNO₃ solution was found to be 83.3 % whereas it was 73.3 % when 0.1 M Cyanex 921 used as synergist under same experimental conditions. The stripping data of Nd(III) from the loaded organic phase containing 0.1 M Cyanex 272 and 0.1 M Cyanex 921/Cyanex 923 with different acids indicated sulphuric acid to be the best stripping agent.     

Prediction of Chromatographic Retention of Pyrazine and Alkylpyrazines in RP-LC

Retention prediction models for a group of pyrazines chromatographed under reversed-phase mode were developed using multiple linear regression (MLR) and artificial neural networks (ANNs). Using MLR, the retention of the analytes were satisfactorily described by a two-predictor model based on the logarithm of the partition coefficient of the analytes (log P) and the percentage of the organic modifier in the mobile phase (ACN or MeOH). ANN prediction models were also derived using the predictors derived from MLR as inputs and log k as outputs. The best network architecture was found to be 2-2-1 for both ACN and MeOH data sets. The optimized ANNs showed better predictive properties than the MLR models especially for the ACN data set. In the case of the MeOH data set, the MLR and ANN models have comparable predictive performance.     

Neural networks for predicting air gap membrane distillation performance

The hollow fiber air gap membrane distillation (AGMD) has recently attracted tremendous attention for desalination and wastewater treatment due to its high packing density, low conductive heat loss, and latent heat recovery capability. Utilizing fast and accurate modeling tools to predict MD performance can result in the further development of desalination technologies. However, simple and time-saving prediction models to assess the AGMD performance were not abundant. Herein, AGMD performance, including permeate flux (J) and gained output ratio (GOR) was predicted through multiple linear regression (MLR) model, back propagation neural network (BP ANN) and radial basis function neural network (RBF ANN) under different hot temperatures (T_h), coolant temperatures (T_c), feed flow rates (F), and feed concentration (c). A total of 30 sets of data were used to train the proposed models, the other 10 external validation datasets not used for training the models were applied to validate the prediction accuracy. The results depicted that RBF ANN (SPREAD = 30, N = 30) showed greatest prediction performance (R² = 0.99–1) compared with BP ANN and MLR models (R² = 0.98–0.99; R² = 0.89–0.97). The computing time consumption of RBF ANN was higher than BP ANN. According to the Mean impact value (MIV) analysis, T_h had the strongest effect on J and GOR. Increasing T_h and decreasing c both had positive impacts on J and GOR, but increasing T_c or F resulted in a trade-off influence. A genetic algorithm (GA) was employed to optimize J and GOR simultaneously, the optimum J and GOR could reach 6.00 kg/m²·h and 7.70 respectively. In this study, the three prediction models proved their abilities to predict AGMD performance and further provide guidance in the actual membrane distillation water treatment process.     

Determination of optimum process conditions for solvent extraction of thorium using Taguchi method

Solvent extraction of thorium was studied using Taguchi method. The effect of various parameters such as acid types (sulfuric, nitric, hydrochloric, sulfuric + nitric) and their concentrations from 0.001 to 4 M, initial thorium concentration (0.0001, 0.001, 0.01, 0.1 M) and solvent type (TBP, D₂EHPA, Cyanex921, Cyanex272) in the ranges of 0.001 to 1 M on thorium extraction efficiency were investigated. The maximum extraction of thorium was obtained while 0.001 M hydrochloric acid, 0.001 or 0.01 M thorium and Cyanex272 were used. Under these optimum conditions, the extraction percent and distribution coefficient of thorium were 98.7% and 73.8, respectively. Compared with the hydrochloric aqueous solution, the nitric acid system showed less variation in the extraction of thorium. The proposed process has been applied for the separation of Th(IV), U(VI), La(III), and Ce(III) from synthetic solution same as thorium ores (monazite).     

Multiple linear regression and artificial neural network retention prediction models for ginsenosides on a polyamine-bonded stationary phase in hydrophilic interaction chromatography

The development of retention prediction models for the seven ginsenosides Rf, Rg1, Rd, Re, Rc, Rb2, and Rb1 on a polyamine-bonded stationary phase in hydrophilic interaction chromatography (HILIC) is presented. The models were derived using multiple linear regression (MLR) and artificial neural network (ANN) using the logarithm of the retention factor (log k) as the dependent variable for four temperature conditions (0, 10, 25, and 40 degrees C). Using stepwise MLR, the retention of the analytes in all the temperature conditions was satisfactorily described by a two-predictor model wherein the predictors were the percentage of ACN (%ACN) in the mobile phase and local dipole index (LDI) of the compounds. These predictors account for the contribution of the solute-related variable (LDI) and the influence of the mobile phase composition (%ACN) on the retention behavior of the ginsenosides. A comparison of the models derived from both MLR and ANN revealed that the trained ANNs showed better predictive abilities than the MLR models in all temperature conditions as demonstrated by their higher R(2) values for both training and test sets and lower average percentage deviation of the predicted log k from the observed log k of the test compounds. The ANN models also showed excellent performance when applied to the prediction of the seven ginsenosides in different sample matrices.     

含氧化合物气相色谱保留指数的预测

通过对部分含氧化合物(醇、酯、醛、酮)在不同固定相不同柱温下的849个样本的气相色谱保留指数值(RI)与其部分参数:拓扑指数(mQ)、定位基参数(Sox)、固定液极性值(CP)及柱温(T)建立定量结构-色谱保留相关(QSRR)模型。分别利用多元线性回归(MLR)、偏最小二乘回归(PLSR)、人工神经网络(ANN)建模,同时采用内部及外部双重验证的办法对所得模型稳定性能进行深入分析和检验,建模计算值、留一法(LOO)交互检验(CV)预测值和外部样本预测值的复相关系数Rcum、QLOO和Rext分别为0.9832、0.9829和0.9836(MLR);0.9832、0.9830和0.9836(PLSR);0.9910、0.9909和0.9900(ANN)。结果表明:所建定量结构保留关系(QSRR)模型具有良好的稳定性和预测能力,较好地揭示了含氧化合物(醇、酯、醛、酮)在不同色谱条件下气相色谱保留指数的变化规律。     

烯烃气相色谱保留指数的QSRR研究

通过对184个烯烃类化合物在不同固定相不同柱温下的617个样本的气相色谱保留指数值(RI)与其部分参数:拓扑指数(mQ)、偶极矩(DPL)、固定液极性值(CP)及柱温(T)建立定量-色谱保留相关(QSRR)模型.分别利用多元线性回归(MLR)、偏最小二乘回归(PLSR)、人工神经网络(ANN)建模,同时采用内部及外部双重验证的办法对所得模型稳定性能进行深入分析和检验,建模计算值、留一法(LOO)交互检验(CV)预测值和外部样本的复相关系数Rcum,QLOO和Rext分别为0.999 2,0.998 4和0.999 2(MLR);0.999 0,0.998 0和0.999 1(PLSR);0.999 4,0.998 7和0.999 2(ANN).结果表明:所建定量结构保留关系(QSRR)模型具有良好的稳定性和预测能力,较好地揭示了烯烃类化合物在不同固定相不同柱温上气相色谱保留指数的变化规律.