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Accelerating Optimization and Uncertainty Quantification of Nonlinear SMB Chromatography Using Reduced-Order Models

MPS-Authors
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Zhang,  Yongjin
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;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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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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Fulltext (public)

zhang_ams_2351807.pdf
(Postprint), 470KB

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Citation

Zhang, Y., Feng, L., Seidel-Morgenstern, A., & Benner, P. (2017). Accelerating Optimization and Uncertainty Quantification of Nonlinear SMB Chromatography Using Reduced-Order Models. Computers & Chemical Engineering, 96, 237-247. doi:10.1016/j.compchemeng.2016.09.017.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002B-A29A-B
Abstract
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