Neurofuzzy Control Strategy for an Abattoir Wastewater Treatment Process

In this paper a neurofuzzy control strategy, composed by a neural observer and a fuzzy supervisor, for an anaerobic wastewater treatment process is proposed. The neural observer is based on a recurrent high order neural network (RHONN) which is trained by an extended Kalman filter. The main objective of the observer is to estimate methanogenic biomass, which is employed by a fuzzy supervisor. The tasks of this supervisor are: to detect the biological activity inside the bioreactor, to select and to apply an adequate control action depending on the operating conditions in order to avoid washout. The applicability of the proposed scheme is illustrated via simulations considering the model of a prototype bioreactor which is used to treat effluents collected from an abattoir.