Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4636
Title: Genetic algorithm-artificial neural network model for the prediction of germanium recovery from zinc plant residues
Authors: Akkurt, Sedat
Özdemir, Serhan
Tayfur, Gökmen
Akkurt, Sedat
Özdemir, Serhan
Tayfur, Gökmen
Izmir Institute of Technology. Mechanical Engineering
Izmir Institute of Technology. Civil Engineering
Keywords: Germanium
Zinc plant residues
Backpropagation
Genetic algorithms
Leaching
Learning algorithms
Neural networks
Issue Date: Sep-2002
Publisher: Taylor and Francis Ltd.
Source: Akkurt, S., Özdemir, S., and Tayfur, G. (2002). Genetic algorithm-artificial neural network model for the prediction of germanium recovery from zinc plant residues. Transactions of the Institution of Mining and Metallurgy, Section C: Mineral Processing and Extractive Metallurgy, 111(3), 129-134. doi:10.1179/037195502766647048
Abstract: A multi-layer, feed-forward, back-propagation learning algorithm was used as an artificial neural network (ANN) tool to predict the extraction of germanium from zinc plant residues by sulphuric acid leaching. A genetic algorithm (GA) was used for the selection of training and testing data and a GA-ANN model of the germanium leaching system was created on the basis of the training data. Testing of the model yielded good error levels (r2 = 0.95). The model was employed to predict the response of the system to different values of the factors that affect the recovery of germanium and the results facilitate selection of the experimental conditions in which the optimum recovery will be achieved.
URI: http://dx.doi.org/10.1179/037195502766647048
http://hdl.handle.net/11147/4636
ISSN: 0371-9553
0371-9553
1743-2855
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Mechanical Engineering / Makina Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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