Removal of Heavy Metals from Mining Wastewater by Micellar-Enhanced Ultrafiltration (MEUF): Experimental Investigation and Monte Carlo-Based Artificial Neural Network Modeling

Abstract

The removal of copper, nickel, and cobalt ions from synthetic mining wastewater was investigated in this study using micellar-enhanced ultrafiltration (MEUF). The effect of surfactant-to-metal (S/M) ratio and pH on metal rejection and permeate flux were examined. A Monte Carlo-based artificial neural network (ANN) modeling approach was proposed to predict the MEUF performance and to reveal the importance of process parameters. The results showed that model-predicted values were in agreement with experimental data (R > 0.99). S/M ratio and pH had relatively greater contributions (30–50%) to the metal rejection rate and permeate flux, whereas sampling time contributed less (10%), which indicated high MEUF efficiency. An S/M ratio of 8.5 with a pH of 8–10 was found to be the optimal condition for MEUF, under which the rejection rates of all three metals exceeded 99% and were in compliance with Canadian environmental standards. Flux decrease and concentration polarization effect were observed during the experimental procedure. Statistical analysis showed that the type of metal examined in this study did not affect MEUF performance.