Optimal design of laboratory and pilot-plant experiments using multiobjective optimization

Performing an experimental design prior to the collection of data is in most circumstances important to ensure efficiency. The focus of this work is the combination of model-based and statistical approaches to optimal design of experiments. The knowledge encoded in the model is used to identify the most interesting range for the experiments via a Pareto optimization of the most important conflicting objectives. Analysis of the trade-offs found is in itself useful to design an experimental plan. This can be complemented using a factorial design in the most interesting part of the Pareto frontier.