Neural Network Modeling for the Extraction of Rare Earth Elements from Eudialyte Concentrate by Dry Digestion and Leaching

• Published in: Metals (Basel ) Vol. 8; no. 4; p. 267
• Main Authors: Ma, Yiqian, Stopic, Srecko, Gronen, Lars, Milivojevic, Milovan, Obradovic, Srdjan, Friedrich, Bernd
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 13.04.2018
• Subjects: Artificial neural networks; Design of experiments; Digestion; dry digestion; eudialyte; Hydrochloric acid; Hydrometallurgy; Laboratories; Leaching; Mathematical models; Modelling; neural network; Neural networks; Rare earth elements
• ISSN: 2075-4701; 2075-4701
• DOI: 10.3390/met8040267

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.