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Publications

Following the links below you find a continuously updated publication list of our group. In order to have an overview of our current publications please visit d|b|t|a's page on ResearchGate: Fachgebiet Dynamik und Betrieb technischer Anlagen

Modeling and Parameter Identification of the Simultaneous Saccharification?Fermentation Process for Ethanol Production
Zitatschlüssel Silvia
Autor S. Ochoa and Y. Ahrim and J.-U. Repke and G. Wozny and Y.-D. Ryook
Seiten 1454-1462
Jahr 2007
DOI 10.1021/bp0702119
Journal Biotechnology Progress
Jahrgang 23
Nummer 6
Zusammenfassung Abstract Despite many environmental advantages of using alcohol as a fuel, there are still serious questions about its economical feasibility when compared with oil?based fuels. The bioethanol industry needs to be more competitive, and therefore, all stages of its production process must be simple, inexpensive, efficient, and “easy” to control. In recent years, there have been significant improvements in process design, such as in the purification technologies for ethanol dehydration (molecular sieves, pressure swing adsorption, pervaporation, etc.) and in genetic modifications of microbial strains. However, a lot of research effort is still required in optimization and control, where the first step is the development of suitable models of the process, which can be used as a simulated plant, as a soft sensor or as part of the control algorithm. Thus, toward developing good, reliable, and simple but highly predictive models that can be used in the future for optimization and process control applications, in this paper an unstructured and a cybernetic model are proposed and compared for the simultaneous saccharification?fermentation process (SSF) for the production of ethanol from starch by a recombinant Saccharomyces cerevisiae strain. The cybernetic model proposed is a new one that considers the degradation of starch not only into glucose but also into dextrins (reducing sugars) and takes into account the intracellular reactions occurring inside the cells, giving a more detailed description of the process. Furthermore, an identification procedure based on the Metropolis Monte Carlo optimization method coupled with a sensitivity analysis is proposed for the identification of the modelapos;s parameters, employing experimental data reported in the literature.
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