Recovery prediction of copper oxide ore column leaching by a hybrid neural genetic algorithm

چکیده مقاله

In this study, mathematical modeling method was used to predict the optimum conditions of column leaching of copper oxide ore. Important parameters such as column height, particle sizes, acid flow rate and leaching time were studied and their impacts on copper recovery were also investigated. The experiments were performed on samples with particle size distributions of minus 25.4 and minus 50.8 millimeter in six columns with the heights of 2, 4 and 6 meter. The results showed that the copper recovery has an inverse relation with column height and particle sizes, and direct relation with leaching time and acid flow rate. The obtained copper recoveries in the columns with heights of 2, 4 and 6 meter were 78.63%, 66.27%, and 52.89%, respectively. According to the results, the trained mathematical models predict the copper recovery based on the operation conditions. Likewise, they showed that the mathematical model could be useful and valid to predict an accurate recovery for the leaching processes.