Study of parameters affecting on copper recovery from oxide ores by column leaching using Artificial Neural Network

In this study, Artificial Neural Network was used for predicting optimized conditions of column leaching on copper oxide ore. Optimization, control and analysis of heap leaching implicate an accurate, proper and comprehensive modeling. Providing such models need to identify all the effective parameters in process and the impact of these factors simultaneously on the output of a process. Important parameters such as height of column, particle’s sizes, acid flow rate and leaching duration were studied and it was investigated their impacts on recovery of copper. Experiments were performed in three columns with the heights of 2, 4 and 6 meters and in the particle size distributions of 25.4 and 50.8 mm. The results showed that the copper recovery has an inverse relation with the column height and particle sizes. This relation is direct with leaching duration and acid rate. The copper recovery obtained in the columns with heights of 2, 4 and 6m were 78.63%, 66.27%, and 52.89% respectively. According to the results, the Trained ANN modeling predicts the copper recovery based on operation conditions.