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

Title:	Artificial Neural Network Prediction of the Performance of Upflow and Downflow Fluidized Bed Reactors Treating Acidic Mine Drainage Water
Authors:	Atasoy, Ayşe Dilek Babar, B. Şahinkaya, Erkan
Keywords:	Metal removal Mine water Reactor modeling Sulfate reduction
Issue Date:	2013
Publisher:	Mine Water Environ
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.
URI:	http://hdl.handle.net/11513/341
Appears in Collections:	Çevre Mühendisliği

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