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

Zhang, Yongjin; Feng, Lihong; Seidel-Morgenstern, Andreas; Benner, Peter

doi:10.1016/j.compchemeng.2016.09.017

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学術論文

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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引用

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.

引用: https://hdl.handle.net/11858/00-001M-0000-002B-A29A-B

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