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.