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    Robust parameter identification using parallel global optimization for a batch nonlinear enzyme-catalytic time-delayed process presenting metabolic discontinuities

    Access Status
    Open access via publisher
    Authors
    Yuan, J.
    Zhang, Y.
    Ye, J.
    Xie, J.
    Teo, Kok Lay
    Zhu, X.
    Feng, E.
    Yin, H.
    Xiu, Z.
    Date
    2017
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Yuan, J. and Zhang, Y. and Ye, J. and Xie, J. and Teo, K.L. and Zhu, X. and Feng, E. et al. 2017. Robust parameter identification using parallel global optimization for a batch nonlinear enzyme-catalytic time-delayed process presenting metabolic discontinuities. Applied Mathematical Modelling. 46: pp. 554-571.
    Source Title
    Applied Mathematical Modelling
    DOI
    10.1016/j.apm.2017.01.079
    Additional URLs
    https://www.sciencedirect.com/science/article/am/pii/S0307904X17300859
    ISSN
    0307-904X
    School
    Department of Mathematics and Statistics
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP140100289
    URI
    http://hdl.handle.net/20.500.11937/53572
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, a nonlinear enzyme-catalytic time-delayed switched dynamical system is considered to describe batch culture of glycerol bioconversion to 1,3-propanediol induced by Klebsiella pneumoniae. This system can not only predict the exponential growth phase but also the lag and the stationary growth phases of batch culture since it contains two switching times for representing the starting moment of lag growth phase and the time when the cell specified growth rate reaches the maximum. The biological robustness is expressed in terms of the expectation and variance of the relative deviation. Our aim is to identify the switching times. To this end, a robust parameter identification problem is formulated, where the switching times are decision variables to be chosen such that the biological robustness measure is optimized. This problem, which is governed by the nonlinear system, is subject to a quality constraint and continuous state inequality constraints. Using a hybrid time-scaling transformation to parameterize the switching times into new parameters, an equivalently robust parameter identification problem is investigated. The continuous state inequality constraints are approximated by a conventional inequality constraint, yielding a sequence of approximate robust parameter identification subproblems. The convergence analysis of this approximation is also investigated. Owing to the highly complex nature of these subproblems, a parallel algorithm, based on simulated annealing, is proposed to solve these subproblems. From an extensive simulation study, it is observed that the obtained optimal switching times are satisfactory.

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