Optimization Using Response Surface Methodology of the Selectivity of Rare Earth Element Dissolution Relative to Naturally Occurring Radioactive Elements (REEs/U+Th) in Red Mud. Case of the Rusal Friguia Bauxite Ore Residues, Republic of Guinea

This study compares the selective dissolution of Rare Earth Elements (REEs) in red mud from alumina production at the Rusal Friguia plant with that of naturally occurring radioactive elements (U, Th) using two leaching agents: nitric acid and citric acid. Leaching tests showed that nitric acid, a strong acid, exhibits high selectivity in favor of REEs over radioactive elements, mainly LREEs/(U+Th), confirming observations reported in the literature. Optimization of operating conditions using the design of experiments methodology identified an optimal point at 25°C for both acids, thereby reducing the severity of the conditions while maintaining good selectivity, and studying the influence of factors (concentration, temperature, and L/S ratio) through a Box-Behnken nonlinear model following the proposed experimental domain. Citric acid, although less effective for dissolving TRs, generated solutions that were significantly poorer in radioactive elements, which is an advantage in terms of minimizing radiological risks. These results suggest that the choice of leaching agent and operating conditions can be guided by the priorities of the process: maximizing TR yield or limiting the co-dissolution of radionuclides. Major Findings: This research presents a novel deep learning-based model for automated detection and classification of moss and crack damage in stone monuments, achieving a high classification accuracy of 97%. The model effectively assesses decay parameters using an MLNN, demonstrating superior performance compared to existing methods like Local Binary Patterns, with high G-mean, Precision, Specificity, Accuracy, F-measure.