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

David Liu*, Yudie Yuan, Shufang Liao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)10397-10400
Number of pages4
JournalExpert Systems with Applications
Issue number7
Publication statusPublished - Sept 2009


  • Gold
  • Neural network
  • Pyrometallurgy

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