title: Hybrid neural modelling of an anaerobic digester with respect to biological constraints.
abstract: A hybrid model for an anaerobic digestion process is proposed. The fermentation is assumed to be performed in two steps, acidogenesis and methanogenesis, by two bacterial populations. The model is based on mass balance equations, and the bacterial growth rates are represented by neural networks. In order to guarantee the biological meaning of the hybrid model (positivity of the concentrations, boundedness, saturation or inhibition of the growth rates) outside the training data set, a method that imposes constraints in the neural network is proposed. The method is applied to experimental data from a fixed bed reactor.
authors.affiliation: LAEP (SACREP), Faculté des Sciences Sémlalia, 40000 Marrakech, Morocco.
authors.forename: A
authors.initials: A
authors.lastname: Karama
authors.forename: O
authors.initials: O
authors.lastname: Bernard
authors.forename: J L
authors.initials: JL
authors.lastname: Gouzé
authors.forename: A
authors.initials: A
authors.lastname: Benhammou
authors.forename: D
authors.initials: D
authors.lastname: Dochain
mesh_terms: D000693:Anaerobiosis; D001421:Bacteria, Anaerobic / Q000254:growth & development / Q000502:physiology*; D001673:Biodegradation, Environmental; D005285:Fermentation; D008954:Models, Biological; D016571:Neural Networks, Computer; D009930:Organic Chemicals; D014874:Water Pollutants, Chemical; D018508:Water Purification / Q000379:methods*
chemical_list: D009930:Organic Chemicals; D014874:Water Pollutants, Chemical