  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.
