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The Scientific World Journal
Volume 2014, Article ID 303858, 9 pages
http://dx.doi.org/10.1155/2014/303858
Research Article

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

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

Received 30 August 2013; Accepted 30 September 2013; Published 5 January 2014

Academic Editors: A.-U. Amin, A. Bekatorou, and A. Tariq

Copyright © 2014 Stefano Curcio et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.