An approach to multicriteria optimization under uncertainty

Inherent in chemical process models are parameters that have uncertainty associated with them. This paper addresses multicriteria optimization that accounts for model and process uncertainty at the design stage. Specifically the authors have developed 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 bounded by 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 assumes that at the operation stage, certain process variables (called control variables) can be tuned or manipulated in order to offset the effects of uncertainty. Three illustrative examples (two benchmark and one direct methanol fuel cell) have been employed.