On the Use of Online Reparametrization in Automated Platforms for Kinetic Model Identification

被引：3

作者：

Quaglio, Marco ^{[1
]}

Waldron, Conor ^{[1
]}

Pankajakshan, Arun ^{[1
]}

Cao, Enhong ^{[1
]}

Gavriilidis, Asterios ^{[1
]}

Fraga, Eric S. ^{[1
]}

Galvanin, Federico ^{[1
]}

机构：

[1] UCL, Dept Chem Engn, Torrington Pl, London WC1E 7JE, England

来源：

CHEMIE INGENIEUR TECHNIK | 2019年 / 91卷 / 03期

基金：

英国工程与自然科学研究理事会;

关键词：

Design of experiments; Identification; Model; Robust parametrization; SEQUENTIAL EXPERIMENTAL-DESIGN; OPTIMUM REFERENCE TEMPERATURE; PRECISE PARAMETER-ESTIMATION; ARRHENIUS EQUATION; REPARAMETERIZATION; OPTIMIZATION;

D O I：

10.1002/cite.201800095

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Parameter estimation algorithms integrated in automated platforms for kinetic model identification are required to solve two optimization problems: i) a parameter estimation problem given the available samples; ii) a model-based design of experiments problem to select the conditions for collecting future samples. These problems may be ill-posed, leading to numerical failures when optimization routines are applied. In this work, an approach of online reparametrization is introduced to enhance the robustness of model identification algorithms towards ill-posed parameter estimation problems.

引用

页码：268 / 276

页数：9

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