Biologically Fe2+ oxidizing fluidized bed reactor performance and controlling of Fe3+ recycle during heap bioleaching: an artificial neural network-based model.
The performance of a biological Fe(2+) oxidizing fluidized bed reactor (FBR) was modeled by a popular neural network-back-propagation algorithm over a period of 220 days at 37 degrees C under different operational conditions. A method is proposed for modeling Fe(3+) production in FBR and thereby managing the regeneration of Fe(3+) for heap leaching application, based on an artificial neural network-back-propagation algorithm. Depending on output value, relevant control strategies and actions are activated, and Fe(3+) production in FBR was considered as a critical output parameter. The modeling of effluent Fe(3+) concentration was very successful, and an excellent match was obtained between the measured and the predicted concentrations.
Ozkaya
Bestamin
B
Institute of Environmental Engineering and Biotechnology, Tampere University of Technology, P.O. Box 541, 33101, Tampere, Finland. bestemin.ozkaya@tut.fi
Sahinkaya
Erkan
E
Nurmi
Pauliina
P
Kaksonen
Anna H
AH
Puhakka
Jaakko A
JA
D000465:Algorithms; D001419:Bacteria / Q000378:metabolism*; D019149:Bioreactors / Q000382:microbiology*; D018929:Cell Culture Techniques / Q000379:methods*; D003198:Computer Simulation; D005246:Feedback / Q000502:physiology; D007501:Iron / Q000378:metabolism*; D008954:Models, Biological; D016571:Neural Networks, Computer; D010084:Oxidation-Reduction
D007501:Iron