Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4636
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dc.contributor.authorAkkurt, Sedat-
dc.contributor.authorÖzdemir, Serhan-
dc.contributor.authorTayfur, Gökmen-
dc.date.accessioned2016-05-12T12:41:18Z
dc.date.available2016-05-12T12:41:18Z
dc.date.issued2002-09
dc.identifier.citationAkkurt, 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:10.1179/037195502766647048en_US
dc.identifier.issn0371-9553
dc.identifier.issn0371-9553-
dc.identifier.issn1743-2855-
dc.identifier.urihttp://dx.doi.org/10.1179/037195502766647048
dc.identifier.urihttp://hdl.handle.net/11147/4636
dc.description.abstractA 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.language.isoenen_US
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofTransactions of the Institution of Mining and Metallurgy, Section C: Mineral Processing and Extractive Metallurgyen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGermaniumen_US
dc.subjectZinc plant residuesen_US
dc.subjectBackpropagationen_US
dc.subjectGenetic algorithmsen_US
dc.subjectLeachingen_US
dc.subjectLearning algorithmsen_US
dc.subjectNeural networksen_US
dc.titleGenetic algorithm-artificial neural network model for the prediction of germanium recovery from zinc plant residuesen_US
dc.typeConference Objecten_US
dc.authoridTR3591en_US
dc.authoridTR130950en_US
dc.authoridTR2054en_US
dc.institutionauthorAkkurt, Sedat-
dc.institutionauthorÖzdemir, Serhan-
dc.institutionauthorTayfur, Gökmen-
dc.departmentİzmir Institute of Technology. Mechanical Engineeringen_US
dc.departmentİzmir Institute of Technology. Civil Engineeringen_US
dc.identifier.volume111en_US
dc.identifier.issue3en_US
dc.identifier.startpage129en_US
dc.identifier.endpage134en_US
dc.identifier.wosWOS:000182201700003en_US
dc.identifier.scopus2-s2.0-0036767574en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1179/037195502766647048-
dc.relation.doi10.1179/037195502766647048en_US
dc.coverage.doi10.1179/037195502766647048en_US
dc.identifier.scopusqualityQ2-
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.dept03.09. Department of Materials Science and Engineering-
crisitem.author.dept03.10. Department of Mechanical Engineering-
crisitem.author.dept03.03. Department of Civil Engineering-
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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