Computation of optimal identification experiments for nonlinear dynamic process models: a stochastic global optimization approach

被引:53
作者
Banga, JR
Versyck, KJ
Van Impe, JF
机构
[1] CSIC, Proc Engn Grp, Inst Invest Marinas, Vigo 36208, Spain
[2] Janssen Pharmaceut, Chem Prod Engn, B-2440 Geel, Belgium
[3] Katholieke Univ Leuven, Dept Chem Engn, BioTeC, B-3001 Heverlee, Belgium
关键词
D O I
10.1021/ie010183d
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
The problem of optimal experimental design (OED) for parameter estimation of nonlinear dynamic systems is considered. It is shown how this problem can be formulated as a dynamic optimization (optimal control) problem where the performance index is usually a scalar function of the Fisher information matrix. Numerical solutions can be obtained using direct methods, which transform the original problem into a nonlinear programming (NLP) problem via parametrizations. However, because of the frequent nonsmoothness of the cost functions, the use of gradient-based methods to solve this NLP might lead to local solutions. Stochastic methods of global optimization are suggested as robust alternatives. A case study considering the OED for parameter estimation in a fed-batch bioreactor is used to illustrate the performance and advantages of two selected stochastic algorithms.
引用
收藏
页码:2425 / 2430
页数:6
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