摘要
The effective treatment of uranium-containing wastewater is of critical importance for environmental radiological protection and the sustainable development of nuclear energy. Nevertheless, achieving directed uranium separation from highly complex systems remains a significant technological challenge. To address this issue, we developed an innovative carbonate-assisted separation strategy specifically for targeted uranium extraction from ultra-complex matrices (13 metal cations and 4 anions). Following the initial separation, uranium retention rate (>50%) in the aqueous phase substantially exceeded that of competing impurity ions, thereby establishing a favorable basis for subsequent purification stages. The separation factors for Cs and Sr were relatively low (3.7 and 7.6), rendering these two elements the hardest impurities to separate from uranium. The separation factors for the remaining impurities range from tens to tens of thousands, indicating excellent separation performance. Further refinement was successfully accomplished through electrochemical processing, which significantly enhanced uranium purity. And the recovery rate could reach over 99%. Comparative experimental studies demonstrated that increased cation concentrations and operating temperatures markedly enhanced the electrochemical extraction efficiency of uranium. To explore the relative importance of experimental parameters, machine learning analysis employing XGBoost gradient boosting combined with SHAP interpretability methods was conducted, revealing that reaction time and electrolyte concentration exerted the greatest influence on the model’s predictions. Characterization results further revealed that carbonate addition simplified uranium reaction pathways to a single dominant process. Collectively, this study establishes a novel conceptual framework and practical methodology for selective uranium separation in uranium-containing wastewater treatment.
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