Extraction chromatography–electrodeposition(EC–ED) process to recover palladium from high-level liquid waste

The extraction chromatography–electrodeposition(EC–ED) process was proposed for the quantitative recovery of palladium from high-level liquid waste(HLLW) in this study. The process coupled the extraction chromatography method to obtain the decontamination of Pd(II) from HLLW with the electrochemical method to recover metallic palladium from the concentrated solution.Separation of Pd(II) from a nitric acid medium by extraction chromatography using iso Bu-BTP/SiO_2-P adsorbent and the electrochemical behavior of Pd(II) in nitric acid solution in the presence of thiourea(TU) were investigated.iso Bu-BTP/SiO_2-P exhibited a high selectivity for Pd(II)over other fission products(FPs), and Pd(II) could be desorbed by TU from loaded BTP/SiO_2-P. The adsorbent performed good stability against HNO_3 because the adsorption performance kept Pd(II) after extended contact with HNO_3 solution. The column experiment achieved the separation of Pd(II) from simulated HLLW successfully.The electrochemical behavior of Pd(II) in palladium desorption solution containing TU and nitric acid was investigated at a platinum electrode by cyclic voltammetry. A weak reduction wave at-0.4 V was due to the reduction in Pd(II) to Pd(0), and the deposition process wasirreversible. In electrowinning experiments, a maximum of92% palladium could be obtained.

Composite Hierachical Linear Quantile Regression

Multilevel （hierarchical） modeling is a generalization of linear and generalized linear modeling in which regression coefficients are modeled through a model, whose parameters are also estimated from data. Multilevel model fails to fit well typically by the use of the EM algorithm once one of level error variance （like Cauchy distribution） tends to infinity. This paper proposes a composite multilevel to combine the nested structure of multilevel data and the robustness of the composite quantile regression, which greatly improves the efficiency and precision of the estimation. The new approach, which is based on the Gauss-Seidel iteration and takes a full advantage of the composite quantile regression and multilevel models, still works well when the error variance tends to infinity, We show that even the error distribution is normal, the MSE of the estimation of composite multilevel quantile regression models nearly equals to mean regression. When the error distribution is not normal, our method still enjoys great advantages in terms of estimation efficiency.