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A new sensitivity-based adaptive control vector parameterization approach for dynamic optimization of bioprocesses

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Abstract

Dynamic optimization is a very effective way to increase the profitability or productivity of bioprocesses. As an important method of dynamic optimization, the control vector parameterization (CVP) approach needs to select an optimal discretization level to balance the computational cost with the desired solution quality. A new sensitivity-based adaptive refinement method is therefore proposed, by which new time grid points are only inserted where necessary and unnecessary points are eliminated so as to obtain economic and effective discretization grids. Moreover, considering that traditional refinement methods may cost a lot to get the high-quality solutions of some bioprocess problems, whose performance indices are sensitive to some significant time points, an optimization technique is further proposed and embedded into the new sensitivity-based CVP approach to efficiently solve these problems. The proposed methods are applied to two well-known bioprocess optimization problems and the results illustrate their effectiveness.

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Acknowledgments

This work is supported by the Major Program of National Natural Science Foundation of China (Grant No. 61590921, 61603336), Zhejiang Province Natural Science Foundation (Y16B040003), Shanghai Aerospace Science and Technology Innovation Fund (E11501) and Aerospace Science and Technology Innovation Fund of China Aerospace Science and Technology Corporation (E11601), and their supports are thereby acknowledged.

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Authors and Affiliations

  1. College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Liwei Wang & Xinggao Liu

  2. Department of Mathematics, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Zeyin Zhang

Authors
  1. Liwei Wang
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  2. Xinggao Liu
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  3. Zeyin Zhang
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Correspondence to Xinggao Liu.

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Wang, L., Liu, X. & Zhang, Z. A new sensitivity-based adaptive control vector parameterization approach for dynamic optimization of bioprocesses. Bioprocess Biosyst Eng 40, 181–189 (2017). https://doi.org/10.1007/s00449-016-1685-7

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  • DOI: https://doi.org/10.1007/s00449-016-1685-7

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