The solvent extraction of Cerium(III) and Lanthanum(III) from nitric acid solution using the organophosphorous extractants Di-(2-ethyl hexyl) phosphate (D2EHPA) and di-2,4,4- trimethylpentyl phosphoric acid (Cyanex272) in kerosene was investigated. In this study, the magnitude of the extraction of Ce(III) was found to be more significant with Cyanex272 than D2EHPA. D2EHPA was found to be a better extractant for La(III). Among the two extractants, Cyanex272 was used for the separation of Ce from La in three stages with an extraction efficiency of 90.2% for Ce. A 556 mg/L Ce solution was used for the scrubbing of La with an efficiency of ≈34%, which required multi stage scrubbing. The study of thermodynamic parameters such as enthalpy, entropy, and Gibbs free energy impart the exothermic and non-spontaneous process. The chemical speciation curves for lanthanum and cerium in the aqueous phase as a function of pH showed that the free La(III) and Ce(III) metal ion species were largely predominate between a pH = 0 and pH = 7.
The dispersion of hazardous gas in the environment presents dangerous risks for people living close to chemical plants or storages. Since heavy gases tend to stay at lower levels and disperse at a slower pace in the atmosphere, they are potentially more dangerous. In this paper, various mathematical models for turbulence (including k-ε, RNG k-ε, EARSM, LES, DES) and their associated parameters have been assessed, compared and validated against the experimental data in various scenarios to find the most suitable one for atmospheric dispersion of dense-gases. This topic has been investigated and validated by a computational fluid dynamics (CFD) simulation of the Kit-Fox experiment. The precision of the CAD models, practicality, computational resource requirements, and some other factors have been considered and addressed in this paper to achieve a comprehensive solution for atmospheric dispersion. The results here suggest that the proper selection of the turbulence model and the turbulent Schmidt number is crucial. Our results indicate that the most promising combination in the case of atmospheric dense-gas dispersion is the RNG k-ε model with the Schmidt number of 0.4, considering the demand for accuracy and computational resource.
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