Multicriteria optimization under uncertainty – considering insufficient process data at the operation stage

Inherent in chemical process models are parameters that have uncertainty associated with them. In an earlier paper the authors addressed multicriteria optimization in the presence of model and process uncertainty at the design stage. Specifically the authors discussed extensions of the average criterion method, the worst-case strategy and the ε -constraint method under the following conditions: (a) at the design stage the only information available about the uncertain parameters is that they are enclosed in a known uncertainty region T, and (b) at the operation stage, process data is rich enough to allow the determination of exact values of all the uncertain parameters. The suggested formulation assumed that at the operation stage, certain process variables (called control variables) could be tuned or manipulated in order to offset the effects of uncertainty. This formulation made the conventional assumption that there was only one type of uncertain parameters. In this follow up paper, the authors consider the more realistic case where the uncertain parameters fall under at least two classes at the operation stage, namely (a) those that can be determined with enough accuracy and (b) those that cannot be determined with such accuracy given the available process data. Three illustrative examples (two benchmark and one direct methanol fuel cell) have been employed.