title: A new neural observer for an anaerobic bioreactor.
abstract: In this paper, a recurrent high order neural observer (RHONO) for anaerobic processes is proposed. The main objective is to estimate variables of methanogenesis: biomass, substrate and inorganic carbon in a completely stirred tank reactor (CSTR). The recurrent high order neural network (RHONN) structure is based on the hyperbolic tangent as activation function. The learning algorithm is based on an extended Kalman filter (EKF). The applicability of the proposed scheme is illustrated via simulation. A validation using real data from a lab scale process is included. Thus, this observer can be successfully implemented for control purposes.
authors.affiliation: Department of Electrical Engineering and Computer Sciences, Cinvestav del IPN, Unidad Guadalajara, Av. Cientifica 1145, Col El Bajio, Zapopan, Jalisco 45015, Mexico. rbelmont@gdl.cinvestav.mx
authors.forename: R
authors.initials: R
authors.lastname: Belmonte-Izquierdo
authors.forename: S
authors.initials: S
authors.lastname: Carlos-Hernandez
authors.forename: E N
authors.initials: EN
authors.lastname: Sanchez
mesh_terms: D000693:Anaerobiosis / Q000502:physiology*; D018533:Biomass; D019149:Bioreactors; D003198:Computer Simulation; D003661:Decision Support Techniques; D006801:Humans; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D013997:Time Factors