Eudialyte is a promising mineral for rare earth elements (REE) extraction due to its good solubility in acid, low radioactive, and relatively high content of REE. In this paper, a two stage hydrometallurgical treatment of eudialyte concentrate was studied: dry digestion with hydrochloric acid and leaching with water. The hydrochloric acid for dry digestion to eudialyte concentrate ratio, mass of water for leaching to mass of eudialyte concentrate ratio, leaching temperature and leaching time as the predictor variables, and the total rare earth elements (TREE) extraction efficiency as the response were considered. After experimental work in laboratory conditions, according to design of experiment theory (DoE), the modeling process was performed using Multiple Linear Regression (MLR), Stepwise Regression (SWR), and Artificial Neural Network (ANN). The ANN model of REE extraction was adopted. Additional tests showed that values predicted by the neural network model were in very good agreement with the experimental results. Finally, the experiments were performed on a scaled up system under optimal conditions that were predicted by the adopted ANN model. Results at the scale-up plant confirmed the results that were obtained in the laboratory.

In this paper, we presented a methodology for efficient and accurate modeling of water losses in hydraulic tunnels under inside internal water pressure, based on multiple linear regression (MLR). The methodology encompasses all steps needed to obtain an adequate mathematical relation between total water losses and relevant measurements in the tunnel, such as reservoir water level, piezometric levels, concrete and water temperatures, size of cracks, etc. Once the data are preprocessed and input variables were chosen, correlation analysis and PCA (principal component analysis) reduction were performed in order to obtain the pool of regression functions. Through an iterative process, according to stepwise regression principles, the most adequate MLR model in terms of accuracy and complexity was chosen. The methodology presented has been validated in modeling water losses in the hydraulic tunnel under the pressure of PSHPP “Bajina Bašta” in the Republic of Serbia. The obtained results have shown significantly better accuracy compared to the results published by other authors, proving that the developed model can be used as a powerful tool in future analyses of tunnel losses and remediation planning.