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 |
Keywords: | Germanium Zinc plant residues Backpropagation Genetic algorithms Leaching Learning algorithms Neural networks |
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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