This work is devoted to the study of the Canet-en-Roussillon (south of France) activated sludge wastewater treatment plant (WWTP) process and to the estimation of chemical parameters (influent and effluent chemical oxygen demand and suspended solids concentration) not easily on-line measurable. Their knowledge makes it possible to estimate both process efficiency and impact on natural environment.

A tool based on neural networks, including an Elman recurrent network, Kohonen's self-organzing maps and a multi level perceptron has been developed. The Elman network is used for the prediction of the incoming WWTP influent flow, specially in case of rain events increasing the quantity of water to be treated. The Kohonen'self-organizing maps neural network is applied to analyse the multi-dimensional Canet-en-Roussillon process data, and to diagnose the inter-relationship of the process variables in an activated sludge WWTP. The multi level perceptron is used as COD and SS estimation tool.