Ruiz Castelló, Pablo Montes Ponce de León, Julio Sanz Bobi, Miguel Ángel 2016-01-15T11:26:14Z 2016-01-15T11:26:14Z 2013-10-20 http://hdl.handle.net/11531/5491 Capítulos en libros Bioethanol production faces control challenges due to its biological alive-process nature and due to the scaling up from the well characterized environment of laboratories to the less controlled industrial facilities. Although second generation technologies -aiming to process lignocellulosic feedstock- are already approaching the market phase, much progress has been done in the first generation technologies, based on starch sacarification. Nonetheless, still room for improvement is left to exploit first generation knowledge embedded in the data gathered during years of continuous operation. In this paper ongoing research for an extensive analysis of such operational data and the possibilities lying in its modeling using Artificial Intelligence (AI) techniques to better explain deviations in the performance of the process is shown. Preliminary results show great possibilities to enlighten the still-grey areas in starch fermentation, while paving the way to extensive application also on second generation technologies. application/pdf en-GB Universidad Pontificia Comillas; IJRER; Gazi University; Nagasaki University; (Madrid, España) Libro: 2nd International Conference on Renewable Energy Research and Applications - ICRERA 2013, Página inicial: 932-937, Página final: Instituto de Investigación Tecnológica (IIT) Bioethanol industrial production optimization info:eu-repo/semantics/bookPart info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/restrictedAccess Bioetahnol; fermentation; fault deteccion; artificial intellingece; renewable energy;