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Efficient Model Reduction of SMB Chromatography by Krylov-subspace Method with Application to Uncertainty Quantification

MPS-Authors
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Yue,  Yao
Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Feng,  Lihong
Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Seidel-Morgenstern,  Andreas
Physical and Chemical Foundations of Process Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Benner,  Peter
Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Yue, Y., Feng, L., Seidel-Morgenstern, A., & Benner, P. (2014). Efficient Model Reduction of SMB Chromatography by Krylov-subspace Method with Application to Uncertainty Quantification. In 24th European Symposium on Computer Aided Process Engineering (pp. 925-930).


Cite as: https://hdl.handle.net/11858/00-001M-0000-0019-F497-4
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