Modeling effluent heavy metal concentrations in a bioleaching process using an artificial neural network technique

Abstract

Artifical neural networks practices were used to predict the recovery of heavy metals (Zn, Cu, Ni, Pb, Cd and Cr) from dewatered metal plating sludge (with no sulfide or sulfate compounds) using bioleaching process involving Acidithiobacillus ferrooxidans. The bioleaching process was operated as a completely mixed batch (CMB) reactor. The leaching performance data of the CMB reactor in terms of heavy metals was applied to a multi-layer perceptron (MLP) neural network technique for simulation. The performance of the reactor was evaluated with this robust model using the experimental data obtained under varying heavy metal  concentrations in the sludge. Agitation time, pulp density of the sludge, and pH were used as inputs for the model, whereas the heavy metals (Cd, Cr, Cu, Ni, Pb, and Zn) concentrations were the output variables. The results of the models were compared using statistical criteria such as mean square error (MSE), mean  absolute error (MAE), mean absolute relative error (MARE), and determination coefficient (R²). The results  show that the MLP neural network produced highly accurate estimation of the aforementioned metals with R2 over 97.9%.

Key words: Bioleaching, heavy metals removal, artificial neural network, multi-layer perceptron.