The artificial neural network(ANN)and hybrid of artificial neural network and genetic algorithm(GANN)were appliedto predict the optimized conditions of column leaching of copper oxide ore with relations of input and o...The artificial neural network(ANN)and hybrid of artificial neural network and genetic algorithm(GANN)were appliedto predict the optimized conditions of column leaching of copper oxide ore with relations of input and output data.The leachingexperiments were performed in three columns with the heights of2,4and6m and in particle size of<25.4and<50.8mm.Theeffects of different operating parameters such as column height,particle size,acid flow rate and leaching time were studied tooptimize the conditions to achieve the maximum recovery of copper using column leaching in pilot scale.It was found that therecovery increased with increasing the acid flow rate and leaching time and decreasing particle size and column height.Theefficiency of GANN and ANN algorithms was compared with each other.The results showed that GANN is more efficient than ANNin predicting copper recovery.The proposed model can be used to predict the Cu recovery with a reasonable error.展开更多
文摘The artificial neural network(ANN)and hybrid of artificial neural network and genetic algorithm(GANN)were appliedto predict the optimized conditions of column leaching of copper oxide ore with relations of input and output data.The leachingexperiments were performed in three columns with the heights of2,4and6m and in particle size of<25.4and<50.8mm.Theeffects of different operating parameters such as column height,particle size,acid flow rate and leaching time were studied tooptimize the conditions to achieve the maximum recovery of copper using column leaching in pilot scale.It was found that therecovery increased with increasing the acid flow rate and leaching time and decreasing particle size and column height.Theefficiency of GANN and ANN algorithms was compared with each other.The results showed that GANN is more efficient than ANNin predicting copper recovery.The proposed model can be used to predict the Cu recovery with a reasonable error.
