| Abstract
| - Decision-making and optimization approaches in chemical product/process design often incorporate probabilitydistributions to express uncertainty about the underlying data. We will show in this paper that probabilitiesare subjective, that they are caused by the decision makers' lack of confidence in the underlying knowledge,and that the shape of the distribution is affected by psychological input factors. Furthermore, distributions donot necessarily express the range of possible values but rather express the values that are perceived to bepossible. Decisions based on distributions can, therefore, be wrong. Screening literature, however, revealsthat the origin of risk and uncertainty is not fully understood and should be discussed. To close this gap, weexplain where risk, distributions, and probabilities come from. On the basis of these insights, we provide anew optimization framework that describes a process of systematically identifying the optimum alternativeby stepwise resolving uncertainty. Finally, we apply the optimization methodology on a case study.
|
