A fast predicting neural fuzzy model for high-rate anaerobic wastewater treatment systems

Based on a conceptual neural fuzzy model developed for anaerobic treatment systems, a fast predicting neural fuzzy model was developed to predict the response of high-rate anaerobic systems to different system disturbances 1 h in advance. The model was applied to three laboratory scale systems, i.e. an anaerobic fluidized bed reactor (AFBR), an anaerobic filter (AF), and an upflow anaerobic sludge blanket (UASB) reactor. In all three cases, the model learned well from the training patterns and exhibited good and fast predictions for the performance of the three systems subjected to a two-fold OLR together with two-fold HLR overload shock. It was proven that neural fuzzy modeling has great adaptability to the variations of system configuration and operation condition. The model is expected to have a great application potential in real time system control.