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

TL;DR

This paper introduces two discretization strategies for optimal model-based experimental design, aiming to efficiently determine the number and selection of experiments in chemical engineering, reducing trial-and-error costs.

Contribution

It proposes two novel discretization methods for experimental design that improve the selection process and compares them with existing pattern-based strategies.

Findings

Discretization strategies effectively identify relevant experiments.

Comparison shows advantages over pattern-based methods.

Validated on chemical engineering examples.

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.