Modelling and simulation of the cyanidation process of Aghdareh gold ore using artificial neural network and multiple linear regression
by Asghar Azizi; Reza Ghaedrahmati; Nader Ghahramani; Reza Rooki
International Journal of Mining and Mineral Engineering (IJMME), Vol. 7, No. 2, 2016

Abstract: This paper describes a simple and more reliable artificial neural network (ANN) method and multiple linear regression (MLR) to predict the gold recovery and to simulate the effects of pH, solid percentage, NaCN concentration, particle size and leaching time during cyanidation process. Feed-forward ANN with back-propagation learning algorithm with 5-9-1 arrangement was found capable to predict the recovery of Au from cyanide leaching solution. The results showed that simulated values obtained by the network were very close to the experimental results. The coefficient of determination value (R²) was 0.9803 for training set, and in testing stage the R² value was 0.8213. On the contrary, the correlation coefficients were low for the results predicted by MLR method. R² values obtained 0.5561 and 0.6705 for training and testing data, respectively. The results obtained from this paper can be considered as an easy and cost-effective method to simulate the cyanide leaching process of gold.

Online publication date: Tue, 10-May-2016

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