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Development of physical models for the process control of a molten carbonate fuel cell system

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons86397

Mangold,  M.
Process Synthesis and Process Dynamics, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons86481

Sheng,  M.
Process Synthesis and Process Dynamics, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons86318

Heidebrecht,  Peter
Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons86359

Kienle,  A.
Process Synthesis and Process Dynamics, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons86497

Sundmacher,  Kai
Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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Citation

Mangold, M., Sheng, M., Heidebrecht, P., Kienle, A., & Sundmacher, K. (2004). Development of physical models for the process control of a molten carbonate fuel cell system. Chemical Engineering Science, 59, 4847-4852. doi:10.1016/j.ces.2004.08.019.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-9E61-9
Abstract
The subject of the paper is the development of suitable models for the process design and process control of molten carbonate fuel cell (MCFC) systems. In a first step, a rigorous dynamic, spatially distributed model of an MCFC is derived from first principles. As this model is too complex for most process control purposes, a reduced model is developed in the following. The reduced model is derived from the spatially distributed reference model of the cell by applying the Karhunen–Loève–Galerkin procedure. The reduced model is of considerably lower order than the original one and requires much less computational time. The applicability of the reduced model to process control problems is demonstrated by using it in the framework of a state and parameter estimator. © 2004 Elsevier Ltd. All rights reserved. [accessed 2014 Januar 10th]