title: Hybrid modelling of anaerobic wastewater treatment processes.
abstract: This paper presents a hybrid approach for the modelling of an anaerobic digestion process. The hybrid model combines a feed-forward network, describing the bacterial kinetics, and the a priori knowledge based on the mass balances of the process components. We have considered an architecture which incorporates the neural network as a static model of unmeasured process parameters (kinetic growth rate) and an integrator for the dynamic representation of the process using a set of dynamic differential equations. The paper contains a description of the neural network component training procedure. The performance of this approach is illustrated with experimental data.
authors.affiliation: University Cadi Ayyad, Département de Physique BP:2390, 4000-Marrakech, Morocco. karama@ucam.ac.ma
authors.forename: A
authors.initials: A
authors.lastname: Karama
authors.forename: O
authors.initials: O
authors.lastname: Bernard
authors.forename: A
authors.initials: A
authors.lastname: Genovesi
authors.forename: D
authors.initials: D
authors.lastname: Dochain
authors.forename: A
authors.initials: A
authors.lastname: Benhammou
authors.forename: J P
authors.initials: JP
authors.lastname: Steyer
mesh_terms: D001421:Bacteria, Anaerobic / Q000378:metabolism*; D007700:Kinetics; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D014865:Waste Disposal, Fluid