Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5627
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dc.contributor.authorBekat, Tuğçe-
dc.contributor.authorErdoğan, Muharrem-
dc.contributor.authorİnal, Fikret-
dc.contributor.authorGenç, Ayten-
dc.date.accessioned2017-05-29T08:51:20Z-
dc.date.available2017-05-29T08:51:20Z-
dc.date.issued2012-09-
dc.identifier.citationBekat, T., Erdoğan, M., İnal, F. and Genç, A. (2012). Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks. Energy, 45(1), 882-887. doi:10.1016/j.energy.2012.06.075en_US
dc.identifier.issn0360-5442-
dc.identifier.urihttp://dx.doi.org/10.1016/j.energy.2012.06.075-
dc.identifier.urihttp://hdl.handle.net/11147/5627-
dc.description.abstracthe amount of bottom ash formed in a pulverized coal-fired power plant was predicted by artificial neural network modeling using one-year operating data of the plant and the properties of the coals processed. The model output was defined as the ratio of amount of bottom ash produced to amount of coal burned (Bottom ash/Coal burned). The input parameters were the moisture contents, ash contents and lower heating values of the coals. The total 653 data were divided into two groups for the training (90% of the data) and the testing (10% of the data) of the network. A three-layer, feed-forward type network architecture with back-propagation learning was used in the modeling study. The activation function was sigmoid function. The best prediction performance was obtained for a one hidden layer network with 29 neurons. The learning rate and the tolerance value were 0.2 and 0.05, respectively. R2 (coefficient of determination) values between the actual (Bottom ash/Coal burned) ratios and the model predictions were 0.988 for the training set and 0.984 for the testing set. In addition, the sensitivity analysis indicated that the ash content of coals was the most effective parameter for the prediction of the ratio of bottom ash to coal burned.en_US
dc.language.isoenen_US
dc.publisherElsevier Ltd.en_US
dc.relation.ispartofEnergyen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectBottom ashen_US
dc.subjectPulverized coal-fired power planten_US
dc.subjectCoalen_US
dc.titlePrediction of the bottom ash formed in a coal-fired power plant using artificial neural networksen_US
dc.typeArticleen_US
dc.authoridTR30587en_US
dc.institutionauthorBekat, Tuğçe-
dc.institutionauthorErdoğan, Muharrem-
dc.institutionauthorİnal, Fikret-
dc.departmentİzmir Institute of Technology. Chemical Engineeringen_US
dc.identifier.volume45en_US
dc.identifier.issue1en_US
dc.identifier.startpage882en_US
dc.identifier.endpage887en_US
dc.identifier.wosWOS:000309243700096en_US
dc.identifier.scopus2-s2.0-84865412043en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1016/j.energy.2012.06.075-
dc.relation.doi10.1016/j.energy.2012.06.075en_US
dc.coverage.doi10.1016/j.energy.2012.06.075en_US
dc.identifier.wosqualityQ1-
dc.identifier.scopusqualityQ1-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.languageiso639-1en-
crisitem.author.dept03.02. Department of Chemical Engineering-
Appears in Collections:Chemical Engineering / Kimya Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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