Genetic Algorithm-Artificial Neural Network Model for the Prediction of Germanium Recovery From Zinc Plant Residues
| dc.contributor.author | Akkurt, Sedat | |
| dc.contributor.author | Özdemir, Serhan | |
| dc.contributor.author | Tayfur, Gökmen | |
| dc.coverage.doi | 10.1179/037195502766647048 | |
| dc.date.accessioned | 2016-05-12T12:41:18Z | |
| dc.date.available | 2016-05-12T12:41:18Z | |
| dc.date.issued | 2002 | |
| dc.description.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. | en_US |
| dc.identifier.citation | 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 | en_US |
| dc.identifier.doi | 10.1179/037195502766647048 | |
| dc.identifier.doi | 10.1179/037195502766647048 | en_US |
| dc.identifier.issn | 0371-9553 | |
| dc.identifier.issn | 1743-2855 | |
| dc.identifier.scopus | 2-s2.0-0036767574 | |
| dc.identifier.uri | http://dx.doi.org/10.1179/037195502766647048 | |
| dc.identifier.uri | https://hdl.handle.net/11147/4636 | |
| dc.language.iso | en | en_US |
| dc.publisher | Taylor and Francis Ltd. | en_US |
| dc.relation.ispartof | Transactions of the Institution of Mining and Metallurgy, Section C: Mineral Processing and Extractive Metallurgy | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Germanium | en_US |
| dc.subject | Zinc plant residues | en_US |
| dc.subject | Backpropagation | en_US |
| dc.subject | Genetic algorithms | en_US |
| dc.subject | Leaching | en_US |
| dc.subject | Learning algorithms | en_US |
| dc.subject | Neural networks | en_US |
| dc.title | Genetic Algorithm-Artificial Neural Network Model for the Prediction of Germanium Recovery From Zinc Plant Residues | en_US |
| dc.type | Conference Object | en_US |
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| gdc.author.institutional | Akkurt, Sedat | |
| gdc.author.institutional | Özdemir, Serhan | |
| gdc.author.institutional | Tayfur, Gökmen | |
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| gdc.description.department | İzmir Institute of Technology. Mechanical Engineering | en_US |
| gdc.description.department | İzmir Institute of Technology. Civil Engineering | en_US |
| gdc.description.endpage | 134 | en_US |
| gdc.description.issue | 3 | en_US |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | N/A | |
| gdc.description.startpage | 129 | en_US |
| gdc.description.volume | 111 | en_US |
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| gdc.oaire.keywords | Germanium | |
| gdc.oaire.keywords | Zinc plant residues | |
| gdc.oaire.keywords | Leaching | |
| gdc.oaire.keywords | Backpropagation | |
| gdc.oaire.keywords | Genetic algorithms | |
| gdc.oaire.keywords | Learning algorithms | |
| gdc.oaire.keywords | Neural networks | |
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| gdc.oaire.sciencefields | 02 engineering and technology | |
| gdc.oaire.sciencefields | 0210 nano-technology | |
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