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

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.