Biologically Fe super(2+) oxidizing fluidized bed reactor performance and controlling of Fe super(3+) recycle during heap bioleaching: an artificial neural network-based model

The performance of a biological Fe super(2+) oxidizing fluidized bed reactor (FBR) was modeled by a popular neural network-back-propagation algorithm over a period of 220days at 37C under different operational conditions. A method is proposed for modeling Fe super(3+) production in FBR and thereby managing the regeneration of Fe super(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 super(3+) production in FBR was considered as a critical output parameter. The modeling of effluent Fe super(3+) concentration was very successful, and an excellent match was obtained between the measured and the predicted concentrations.