Recovery prediction of copper oxide ore column leaching by hybrid neural genetic algorithm
(1. Department of Mining & Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran;
2. Department of Mining Engineering, University of Kashan, Kashan, Iran;
3. Department of Chemical and Materials Engineering, University of Alberta Edmonton, Alberta T6E 2H8, Canada)
2. Department of Mining Engineering, University of Kashan, Kashan, Iran;
3. Department of Chemical and Materials Engineering, University of Alberta Edmonton, Alberta T6E 2H8, Canada)
Abstract: The artificial neural network (ANN) and hybrid of artificial neural network and genetic algorithm (GANN) were applied to predict the optimized conditions of column leaching of copper oxide ore with relations of input and output data. The leaching experiments were performed in three columns with the heights of 2, 4 and 6 m and in particle size of <25.4 and <50.8 mm. The effects of different operating parameters such as column height, particle size, acid flow rate and leaching time were studied to optimize the conditions to achieve the maximum recovery of copper using column leaching in pilot scale. It was found that the recovery increased with increasing the acid flow rate and leaching time and decreasing particle size and column height. The efficiency of GANN and ANN algorithms was compared with each other. The results showed that GANN is more efficient than ANN in predicting copper recovery. The proposed model can be used to predict the Cu recovery with a reasonable error.
Key words: leaching; copper oxide ore; recovery; artificial neural network; genetic algorithm