Artificial neural network simulation of rare earths solvent extraction equilibrium data Original Research Article

Separation of high purity rare earth elements from their mixed oxides, obtained from monazite or xenotime, requires multiple stages of separation by circuits incorporating one or more solvents. The separation factors being small, a large number of counter-current stages become necessary. Process development, analysis, optimization and control of rare earths are a complex task. Computer simulation provides useful tools in this area. Application of artificial neural networks (ANN) for simulation of equilibrium data in solvent extraction of rare earths is described in this paper. The back propagation ANN model has been used. The input neurons correspond to the system state variables such as equilibrium concentration and acidity. The partitioning of the metal ion into the two immiscible phases involved in solvent extraction is measured in terms of distribution ratio D. The model predicts the D value under varying process conditions. Comparison of ANN with conventional models shows that ANN is superior. The average absolute error for ANN model is one-fourth that of the conventional models. The approach has been used, in conjunction with a process simulation model, successfully for industrial process development involving production of high purity neodymium.