Measure this, not that: Optimizing the cost and model-based information content of measurements

• Published in: Computers & chemical engineering Vol. 189; no. C; p. 108786
• Main Authors: Wang, Jialu, Peng, Zedong, Hughes, Ryan, Bhattacharyya, Debangsu, Bernal Neira, David E., Dowling, Alexander W.
• Format: Journal Article
• Language: English
• Published: United Kingdom Elsevier Ltd 01.10.2024; Elsevier
• Subjects: Convex optimization; Data science; Digital twins; Fisher information matrix; Measurement optimization; Sensor network design; Data science; Measurement optimization; Sensor network design; Digital twins; Convex optimization; Fisher information matrix
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2024.108786

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, 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 CO2 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. [Display omitted] •Proposes convex MI(N)LP problems to optimize the Fisher information of measurements.•Uses grey-box module in Pyomo for externally evaluated objective function.•Optimizes with highly correlated measurements on kinetics case study.•Demonstrates scalability in CO2 capture case study.