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

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. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Automated summary

Model-based experimental design in chemical process engineering is gaining attention for its iterative approach in refining models through prescribed experiments. Strategies for design of experiments focus on maximizing information gain, involving solving large nonlinear optimization problems. Two discretization strategies have been proposed to assist experimenters in setting relevant experiments and optimal selection, showing validity in academic and chemical engineering test problems.