title: Selecting the most relevant variables for anaerobic digestion imbalances: two case studies.
abstract: In this study, a wrapper approach was applied to objectively select the most important variables related to two different anaerobic digestion imbalances, acidogenic states and foaming. This feature selection method, implemented in artificial neural networks (ANN), was performed using input and output data from a fully instrumented pilot plant (1 m3 upflow fixed bed digester). Results for acidogenic states showed that pH, volatile fatty acids, and inflow rate were the most relevant variables. Results for foaming showed that inflow rate and total organic carbon were among the relevant variables, both of which were related to the feed loading of the digester. Because there is not a complete agreement on the causes of foaming, these results highlight the role of digester feeding patterns in the development of foaming.
authors.affiliation: Laboratory of Chemical and Environmental Engineering (LEQUIA), University of Girona, Girona, Catalonia, Spain.
authors.forename: Jordi
authors.initials: J
authors.lastname: Dalmau
authors.forename: Joaquim
authors.initials: J
authors.lastname: Comas
authors.forename: Ignasi
authors.initials: I
authors.lastname: Rodríguez-Roda
authors.forename: Eric
authors.initials: E
authors.lastname: Latrille
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Steyer
mesh_terms: D000693:Anaerobiosis; D019149:Bioreactors; D003198:Computer Simulation; D006863:Hydrogen-Ion Concentration; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D014865:Waste Disposal, Fluid / Q000295:instrumentation / Q000379:methods*