title: Neural and hybrid modeling: an alternative route to efficiently predict the behavior of biotechnological processes aimed at biofuels obtainment.
abstract: The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.
authors: Laboratory of Transport Phenomena and Biotechnology, Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, Ponte P. Bucci, Cubo 39/C, 87036 Rende, Italy.
authors: Stefano
authors: S
authors: Curcio
authors: Laboratory of Transport Phenomena and Biotechnology, Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, Ponte P. Bucci, Cubo 39/C, 87036 Rende, Italy.
authors: Alessandra
authors: A
authors: Saraceno
authors: Laboratory of Transport Phenomena and Biotechnology, Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, Ponte P. Bucci, Cubo 39/C, 87036 Rende, Italy.
authors: Vincenza
authors: V
authors: Calabrò
authors: Laboratory of Transport Phenomena and Biotechnology, Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, Ponte P. Bucci, Cubo 39/C, 87036 Rende, Italy.
authors: Gabriele
authors: G
authors: Iorio
mesh_terms: D056804:Biofuels; D001709:Biotechnology / Q000379:methods; D008962:Models, Theoretical
chemical_list: D056804:Biofuels