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 on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In parti...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/303858 |
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| _version_ | 1832558925681000448 |
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| author | Stefano Curcio Alessandra Saraceno Vincenza Calabrò Gabriele Iorio |
| author_facet | Stefano Curcio Alessandra Saraceno Vincenza Calabrò Gabriele Iorio |
| author_sort | Stefano Curcio |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-48634aa21d36447ab1042e40306ec446 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-48634aa21d36447ab1042e40306ec4462025-02-03T01:31:15ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/303858303858Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels ObtainmentStefano Curcio0Alessandra Saraceno1Vincenza Calabrò2Gabriele Iorio3Laboratory of Transport Phenomena and Biotechnology, Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, Ponte P. Bucci, Cubo 39/C, 87036 Rende, ItalyLaboratory of Transport Phenomena and Biotechnology, Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, Ponte P. Bucci, Cubo 39/C, 87036 Rende, ItalyLaboratory of Transport Phenomena and Biotechnology, Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, Ponte P. Bucci, Cubo 39/C, 87036 Rende, ItalyLaboratory of Transport Phenomena and Biotechnology, Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, Ponte P. Bucci, Cubo 39/C, 87036 Rende, ItalyThe 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.http://dx.doi.org/10.1155/2014/303858 |
| spellingShingle | Stefano Curcio Alessandra Saraceno Vincenza Calabrò Gabriele Iorio Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment The Scientific World Journal |
| title | Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment |
| title_full | Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment |
| title_fullStr | Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment |
| title_full_unstemmed | Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment |
| title_short | Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment |
| title_sort | neural and hybrid modeling an alternative route to efficiently predict the behavior of biotechnological processes aimed at biofuels obtainment |
| url | http://dx.doi.org/10.1155/2014/303858 |
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