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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.isoengen_US
dc.publisherElsevieren_US
dc.relation.isversionof10.1016/j.energy.2012.06.075en_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.contributor.authorIDTR30587en_US
dc.contributor.iztechauthorBekat, Tuğçe
dc.contributor.iztechauthorErdoğan, Muharrem
dc.contributor.iztechauthorİnal, Fikret
dc.relation.journalEnergyen_US
dc.contributor.departmentİYTE, Mühendislik Fakültesi, Kimya Mühendisliği Bölümüen_US
dc.identifier.volume45en_US
dc.identifier.issue1en_US
dc.identifier.startpage882en_US
dc.identifier.endpage887en_US
dc.identifier.wosWOS:000309243700096
dc.identifier.scopusSCOPUS:2-s2.0-84865412043
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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