Measure This, Not That: Optimizing the Cost and Model-Based Information Content of Measurements

TL;DR

This paper enhances model-based experimental design by formulating a convex MINLP optimization problem, enabling efficient measurement selection to maximize information content within budget constraints, demonstrated through two case studies.

Contribution

It introduces a convex MINLP formulation for measurement optimization in MBDoE and modifies the solver MindtPy to compute D-optimality objectives using SciPy.

Findings

Effective measurement selection in two case studies.

Trade-offs between information content and measurement cost.

Practical guidance for scientific experiment design.

Abstract

Model-based design of experiments (MBDoE) is a powerful framework for selecting and calibrating science-based mathematical models from data. This work extends popular MBDoE workflows by proposing a convex mixed integer (non)linear programming (MINLP) problem to optimize the selection of measurements. The solver MindtPy is modified to support calculating the D-optimality objective and its gradient via an external package, \texttt{SciPy}, using the grey-box module in Pyomo. The new approach is demonstrated in two case studies: estimating highly correlated kinetics from a batch reactor and estimating transport parameters in a large-scale rotary packed bed for CO$_2$ capture. Both case studies show how examining the Pareto-optimal trade-offs between information content measured by A- and D-optimality versus measurement budget offers practical guidance for selecting measurements for scientific experiments.