An online reparametrisation approach for robust parameter estimation in automated model identification platforms
An online reparametrisation approach for robust parameter estimation in automated model identification platforms
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DOI：
10.1016/j.compchemeng.2019.01.010
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Marco Quaglio;C. Waldron;A. Pankajakshan;E. Cao;A. Gavriilidis;E. Fraga;F. Galvanin
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文献类型：
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作者：
Marco Quaglio;C. Waldron;A. Pankajakshan;E. Cao;A. Gavriilidis;E. Fraga;F. Galvanin
自动模型识别平台最近被用于在无人实验战役过程中在线识别参数模型。控制这些平台的算法包括两个计算元素：i)参数估计工具；ii)基于模型的实验设计工具。这两种工具都需要解决复杂的优化问题，它们的有效结果依赖于它们各自的目标函数条件良好。当模型以弱参数化为特征时，即模型参数实际上不可识别和/或极度相关时，可能会出现病态目标函数。在这项工作中，提出了一种健壮的再参数技术，并在计算机内和自动模型识别平台上进行了测试。以流动微反应器中苯甲酸与乙醇催化酯化反应动力学模型的辨识为例，证明了再参数化法的优越性。
Automated model identification platforms were recently employed to identify parametric models online in the course of unmanned experimental campaigns. The algorithms controlling these platforms include two computational elements:i) a tool for parameter estimation;ii) a tool for model-based experimental design. Both tools require the solution of complex optimisation problems and their effective outcome relies on their respective objective functions being well-conditioned. Ill-conditioned objective functions may arise when the model is characterised by a weak parametrisation, i.e. the model parameters are practically non-identifiable and/or extremely correlated. In this work, a robust reparametrisation technique is proposed and tested both in-silico and in an automated model identification platform. The benefit of reparametrisation is demonstrated on a case study for the identification of a kinetic model of catalytic esterification of benzoic acid with ethanol in a flow microreactor.