Two-phase approaches to optimal model-based design of experiments: how many experiments and which ones?

Charlie Vanaret, Philipp Seufert, Jan Schwientek, Gleb Karpov, Gleb Ryzhakov, Ivan Oseledets, Norbert Asprion, Michael Bortz

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

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 to 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. It requires the resolution of a large nonlinear, generally nonconvex, optimization problem, whose solution may greatly depend on the starting point. We present two discretization strategies that can assist the experimenter in setting the number of relevant experiments and performing an optimal selection, and we compare them against two pattern-based strategies that are independent of the problem. The validity of the approaches is demonstrated on an academic example and two test problems from chemical engineering including a vapor liquid equilibrium and reaction kinetics.

Original languageEnglish
Article number107218
JournalComputers and Chemical Engineering
Volume146
DOIs
Publication statusPublished - Mar 2021
Externally publishedYes

Keywords

  • Discretized approximations
  • Equivalence theorem
  • Experimental design
  • Initialization strategies
  • Nonconvex optimization problem
  • Proof of optimality
  • Two-phase approaches

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