Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment

The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or onhybrid 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 forthe obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was provedthat the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybridmodeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge ofthe metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processeswas difficult to be achieved.