Modeling and optimization of neodymium ion separation by liquid membrane using Artificial Neural Network coupled with Genetic Algorithm

Abstract

The synergistic effects of two or more carrier species significantly intensify the performance of liquid membranes. However, this process is difficult to model as different carrier species make different complexes with permeating species. Artificial Neural Network (ANN) based modeling is helpful in this case. First number of neurons in the hidden layer was optimized to reduce mean squared error (MSE). Minimum MSE (10.96) was achieved with ten neurons. Simulations showed that neodymium transport initially increased with increasing carrier species TOPO concentration but later decreased due to increased viscosity of liquid membrane. Similarly, higher neodymium transport was found at a moderate concentration of H2SO4 in receiving phase and a low concentration of HCl in the feed phase. Trained ANN was coupled with the Genetic Algorithm to determine the optimum operating parameters in their given range. 0.12 M TOPO concentration in the liquid membrane, 4.2 M of H2SO4 concentration in the receiving phase, and 0.5 M of HCl concentration in the feed phase were optimum values of the operating parameters to achieve 100% neodymium transport through the liquid membrane in 360 min operation.