Reduced-Order Modeling and ROM-Based Optimization of Batch Chromatography

  • Peter Benner
  • Lihong Feng
  • Suzhou Li
  • Yongjin ZhangEmail author
Conference paper
Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 103)


A reduced basis method is applied to batch chromatography and the underlying optimization problem is solved efficiently based on the resulting reduced model. A technique of adaptive snapshot selection is proposed to reduce the complexity and runtime of generating the reduced basis. With the help of an output-oriented error bound, the construction of the reduced model is managed automatically. Numerical examples demonstrate the performance of the adaptive technique in reducing the offline time. The ROM-based optimization is successful in terms of the accuracy and the runtime for getting the optimal solution.


Posteriori Error Estimation Reduce Basis Parabolic Partial Differential Equation Reduce Basis Method Random Sample Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Peter Benner
    • 1
  • Lihong Feng
    • 1
  • Suzhou Li
    • 1
  • Yongjin Zhang
    • 1
    Email author
  1. 1.Max Planck Institute for Dynamics of Complex Technical SystemsMagdeburgGermany

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