Artificial neural network modeling of gold dissolution in cyanide media

作     者：S.Khoshjavan M.Mazloumi B.Rezai 

作者机构：Department of Mining and Metallurgy Engineering Amirkabir University of Technology Department of Mining Engineering Faculty of Engineering University of Tehran 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2011年第18卷第6期

页      面：1976-1984页

核心收录：

学科分类：12[管理学] 080603[工学-有色金属冶金] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0806[工学-冶金工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：artificial neural network gold cyanidation modeling sensitivity analysis 

摘      要：The effects of cyanidation conditions on gold dissolution were studied by artificial neural network （ANN） modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid percentage, P50 of particle, NaCN content in cyanide media, temperature of solution and pH value were used. For selecting the best model, the outputs of models were compared with measured data. A fourth-layer ANN is found to be optimum with architecture of twenty, fifteen, ten and five neurons in the first, second, third and fourth hidden layers, respectively, and one neuron in output layer. The results of artificial neural network show that the square correlation coefficients （R2） of training, testing and validating data achieve 0.999 1, 0.996 4 and 0.9981, respectively. Sensitivity analysis shows that the highest and lowest effects on the gold dissolution rise from time and pH, respectively It is verified that the predicted values of ANN coincide well with the experimental results.