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Two-Phase Approaches to Optimal Model-Based Design of Experiments: How Many Experiments and Which Ones?

EasyChair Preprint 2518

2 pagesDate: January 31, 2020

Abstract

Model-based experimental design is attracting increasing attention in chemical process engineering. Typically, an iterative procedure is pursued: an approximate model is devised, prescribed experiments are then performed and the resulting data is exploited to refine the model. To help reduce the cost of trial-and-error approaches, strategies for model-based design of experiments suggest experimental points where the expected gain in information for the model is the largest. From a technical perspective, it requires the resolution of a large nonlinear, generally nonconvex, optimization problem, whose solution may greatly depend on the starting point.

Keyphrases: approximation strategies, equivalence theorem, model-based experimental design

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@booklet{EasyChair:2518,
  author    = {Charlie Vanaret and Philipp Seufert and Jan Schwientek and Gleb Karpov and Gleb Ryzhakov and Ivan Oseledets and Norbert Asprion and Michael Bortz},
  title     = {Two-Phase Approaches to Optimal Model-Based Design of Experiments: How Many Experiments and Which Ones?},
  howpublished = {EasyChair Preprint 2518},
  year      = {EasyChair, 2020}}
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