Artificial neural network vs. nonlinear regression for gold content estimation in pyrometallurgy

Abstract

Pyrometallurgy is often used in the industrial process for treating gold-bearing slime. Slag compositions have remarkable influences on gold recovery and gold content in slag. In this paper, the relationships between the slag compositions in the soda–borax–silica glass-salt system and the gold content in the slag are investigated by using nonlinear regression and artificial neural network. A neural network model for estimating the gold contents of different slag compositions is presented, including the neural network type, structure and its learning algorithms. The study indicates that the three-layer back propagation neural network model can be applied to estimate gold content in the slag. Compared with the traditional regression methods, the neural network has many advantages.