Recurrent neuro-fuzzy modeling of a wastewater treatment plant

This paper deals with the use of a special kind of recurrent neuro-fuzzy model to represent complex systems. The neuro-fuzzy system, called RFasArt, has been used in this work to model a complex biotechnological process: an activated sludge process taken from a real wastewater treatment plant. This network is based on the adaptive resonance theory (ART) but it also introduces formalisms from the fuzzy set theory and takes into account the available contextual information in its processing stage. Real data records taken from the plant were used to train this network. he results obtained with this recurrent fuzzy neural network have been compared with the ones obtained with a classical recurrent neural network, showing the advantageous behaviour of the RFasArt System. Apart from modelling, the RFasArt architecture provides a knowledge base of fuzzy rules containing information about the plant dynamic behaviour.