Optimizing of Iron Bioleaching from a Contaminated Kaolin Clay by the Use of Artificial Neural Network

In this research, the amount of Iron removal by bioleaching of a kaolin sample with high iron impurity with Aspergillus niger was optimized. In order to study the effect of initial pH, sucrose and spore concentration on iron, oxalic acid and citric acid concentration, more than twenty experiments were performed. The resulted data were utilized to train, validate and test the two layer artificial neural network (ANN). In order to minimize the overfitting, Bayesian regularization and early stopping methods with back propagation algorithm were utilized as training algorithm of ANN. Good validation for prediction of iron removal percent was resulted due to the inhibition of over-fitting problems with selection of appropriate ANN topology and training algorithm. The results showed that optimized condition of initial pH, sucrose and spore concentration to achieve high Iron removal should be 6, 60 g/l and 3.5×107, respectively.