Neural Network Identification of N-Removal Process in Waste Water Treatment Plants

Alejandro Goldar, Silvana R. Revollar, Rosalba Lamanna, and Pastora Vega


WWTP, Activated sludge treatment, ANN


A nonlinear model with neural networks structure is identified in this paper from input-output data of the activated-sludge process. The simulation protocol BSM1 is used as a benchmark to gather the operating data used for the neural networks training and validations. The neural model is constituted by two feed-forward neural networks to estimate oxygen and nitrates concentrations. The performance of each NN is assessed as a virtual sensor for variable estimation, or as a predictive model for process control purposes.

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