Caustic-Side Solvent Extraction: Extended Equilibrium Modeling of Cesium and Potassium Distribution Behavior

Delmau, L. H.

Oak Ridge National Laboratory

关键词: Forecasting;    Sodium;    Potassium;    Anions;    Simulation;   

DOI  :  10.2172/814253
RP-ID  :  ORNL/TM-2002/116
RP-ID  :  AC05-00OR22725
RP-ID  :  814253

美国|英语

来源: UNT Digital Library

【 摘 要 】

An extension of the model developed in FY01 for predicting equilibrium distribution ratios in the Caustic-Side Solvent Extraction (CSSX) process is presented here. Motivation for extending the model arose from the need to predict extraction performance of the recently optimized solvent composition and the desire to include additional waste components. This model involves the extraction of cesium and potassium from different cesium, potassium, and sodium media over a large range of concentrations. Those different media include a large variety of anions such as nitrate, hydroxide, nitrite, chloride, fluoride, sulfate, and carbonate. The model was defined based on several hundreds of experimental data points and predicted satisfactorily the cesium extraction from five different SRS waste simulants. This process model encompassed almost exclusively 1:1:1 metal:anion:ligand species. Fluoride, sulfate, and carbonate species were found to be very little extractable, and their main impact is reflected through their activity effects. This model gave a very good cesium and potassium extraction prediction from sodium salts, which is what is needed when trying to predict the behavior from actual waste. However, the extraction from potassium or cesium salts, and the extraction of sodium could be improved, and some additional effort was devoted to improve the thermodynamic rigor of the model. Toward this end, more detailed anion-specific models were developed based on the cesium, potassium, and sodium distribution ratios obtained with simple systems containing single anions, but it has not yet proven possible to combine those models to obtain better predictions than provided by the process model.