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

EasyChair Preprint no. 2518

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

@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 no. 2518},

  year = {EasyChair, 2020}}