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

Authors:
David Liu;Yudie Yuan;Shufang Liao
Affiliations:
Department of Mathematical Sciences, Xi'an Jiaotong - Liverpool University, 111 Ren Ai Road, Dushu Lake Higher Education Town, Suzhou Industrial Park, Suzhou, Jiangsu, PR China and School of Scien ...;Novelis Global Technology Centre, 945 Princess St., P.O. Box 8400, Kingston, Ontario, Canada K7L 5L9;Department of Mathematical Sciences, Xi'an Jiaotong - Liverpool University, 111 Ren Ai Road, Dushu Lake Higher Education Town, Suzhou Industrial Park, Suzhou, Jiangsu, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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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.