Modeling of synergistic solvent extraction of Zr(IV) / HCl / TOA+Cyanex 921 system using statistical analysis and neural network

Abstract

Multi-linear regression analysis (MLR), radial basis function (RBF) and multilayer perceptron (MLP) of artificial neural network (ANN) with five inputs (temperature, concentrations of HCl, TOA, Cyanex 921, Zr (IV) and percentage of extraction (%E)) as only output were employed for the construction of models. It was observed that ANN (RBF and MLP) performed better as compared to the MLR model. Based on the models proposed, the extraction of Zr(IV) could be predicted under variable experimental conditions of concentrations of HCl, TOA (Tri-n-octylamine), Cyanex 921 (Tri-n-octyl phosphineoxide), Zr(IV) and temperature. The nonlinear and complex relation between the percentage of extraction and operating variables have been determined using two and three layered feed forward neural network with back-propagation of error learning algorithm. Uncertainties in data have been determined in terms of statistical parameters such as root mean-squared error and R-squared values to check the efficiency of the model for prediction.