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Multi-fidelity Modeling and Optimization of Biogas Plants

  • An essential task for operation and planning of biogas plants is the optimization of substrate feed mixtures. Optimizing the monetary gain requires the determination of the exact amounts of maize, manure, grass silage, and other substrates. Accurate simulation models are mandatory for this optimization, because the underlying chemical processes are very slow. The simulation models themselves may be time-consuming to evaluate, hence we show how to use surrogate-model-based approaches to optimize biogas plants efficiently. In detail, a Kriging surrogate is employed. To improve model quality of this surrogate, we integrate cheaply available data into the optimization process. Doing so, Multi-fidelity modeling methods like Co-Kriging are employed. Furthermore, a two-layered modeling approach is employed to avoid deterioration of model quality due to discontinuities in the search space. At the same time, the cheaply available data is shown to be very useful for initialization of the employed optimization algorithms. Overall, we show how biogas plants can be efficiently modeled using data-driven methods, avoiding discontinuities as well as including cheaply available data. The application of the derived surrogate models to an optimization process is shown to be very difficult, yet successful for a lower problem dimension.

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Author:Martin Zaefferer, Daniel Gaida, Thomas Bartz-BeielsteinGND
Series (Serial Number):CIplus (2/2014)
Document Type:Preprint
Year of Completion:2014
Release Date:2014/10/24
Tag:Biogas Plant; Co-Kriging; Multi-fidelity; Optimization; Simulation; Surrogate Models
GND Keyword:Biogas; Simulation; Modellierung; Optimierung; Kriging
Contributor:Bartz-Beielstein, Thomas
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10) / Fakultät 10 / Institut für Informatik
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Open Access:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung