Artificial neural networks for optimization of gold-bearing slime smelting

David Liu*, Yudie Yuan, Shufang Liao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Pyrometallurgy is often used in the industrial process for treating gold-bearing slime. Slag compositions have remarkable influences on the recovery of gold and the gold content in slag. A method for determining optimum flux compounding with neural networks is studied in this paper, and the neural network model for estimating the gold contents with different slag compositions is presented. On the basis of the neural network model, an algorithm for searching the optimum flux compounding in the gold-slime smelting process is proposed, and the optimum flux compositions are obtained accordingly.

Original languageEnglish
Pages (from-to)11671-11674
Number of pages4
JournalExpert Systems with Applications
Issue number9
Publication statusPublished - Nov 2009


  • Gold
  • Gold slime
  • Neural network
  • Optimum flux composition

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