Mathematical models are of widespread usage for simulating process behavior, designing new processes and equipment and, in a more general sense, decision making. However, as model parameters are uncertain, due to model inaccuracies and experimental errors, all model results are subject to uncertainties. It is shown here that dir economical value may be assigned to parameter uncertainties, which can then be used for both process optimization and specially for taking decisions during sequential experimental designs.