Artificial Neural Network Prediction of the Performance of Upflow and Downflow Fluidized Bed Reactors Treating Acidic Mine Drainage Water

Please use this identifier to cite or link to this item: http://hdl.handle.net/11513/341

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DC Field	Value	Language
dc.contributor.author	Atasoy, Ayşe Dilek	-
dc.contributor.author	Babar, B.	-
dc.contributor.author	Şahinkaya, Erkan	-
dc.date.accessioned	2019-06-19T10:33:40Z	-
dc.date.available	2019-06-19T10:33:40Z	-
dc.date.issued	2013	-
dc.identifier.other	DOI 10.1007/s10230-013-0232-x	-
dc.identifier.uri	http://hdl.handle.net/11513/341	-
dc.description.abstract	The performance of fluidized bed reactors treating synthetic acid mine drainage were predicted using an artificial neural network (ANN). The developed model gave satisfactory fits to the experimentally obtained sulfate, COD, alkalinity, and sulfide data; R-values were within 0.92 and 0.98. ANN can be effectively used to predict the performance of these complex systems and, with the proposed model-based applications, it is possible to reduce operational costs and risks.	en_US
dc.language.iso	en	en_US
dc.publisher	Mine Water Environ	en_US
dc.subject	Metal removal	en_US
dc.subject	Mine water	en_US
dc.subject	Reactor modeling	en_US
dc.subject	Sulfate reduction	en_US
dc.title	Artificial Neural Network Prediction of the Performance of Upflow and Downflow Fluidized Bed Reactors Treating Acidic Mine Drainage Water	en_US
dc.type	Article	en_US
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