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

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-002B-A29A-B Version Permalink: https://hdl.handle.net/21.11116/0000-0002-87C1-B
Genre: Journal Article

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 Creators:
Zhang, Yongjin1, Author           
Feng, Lihong1, Author           
Seidel-Morgenstern, Andreas2, 3, Author           
Benner, Peter1, Author           
Affiliations:
1Computational Methods in Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738141              
2Physical and Chemical Foundations of Process Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738150              
3Otto-von-Guericke-Universität Magdeburg, External Organizations, ou_1738156              

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 Dates: 2017
 Publication Status: Issued
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 Identifiers: DOI: 10.1016/j.compchemeng.2016.09.017
Other: pubdata_escidoc:2351807
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Title: Computers & Chemical Engineering
Source Genre: Journal
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Pages: - Volume / Issue: 96 Sequence Number: - Start / End Page: 237 - 247 Identifier: -