Modeling the input-output behaviour of wastewater treatment plants using soft computing techniques

Abstract

Wastewater Treatment Plants (WWTPs) control and prediction under a wide range of operating conditions is an important goal in order to avoid breaking of environmental balance, keep the system in stable operating conditions and suitable decision-making. In this respect, the availability of models characterizing WWTP behaviour as a dynamic system, is a necessary first step. However, due to the high complexity of the WWTP processes and the heterogeneity, incompleteness and imprecision of WWTP data, finding suitable models poses substantial problems. In this paper, an approach via soft computing techniques is sought, in particular, by experimenting with fuzzy heterogeneous time-delay neural networks to characterize the time variation of outgoing variables. Experimental results show that these networks are able to characterize WWTP behaviour in a statistically satisfactory sense and also that they perform better than other well-established neural network mode.