Efficient approach for calculating Pareto boundaries under uncertainties in chemical process design

Taking account of uncertain model parameters in simulation-based flowsheet optimization is crucial in order to quantify the reliability of the optimization results. Since chemical process design is a multicriteria optimization (MCO) task, methods to deal with uncertain Pareto boundaries are needed. The simplest of such methods consists of a sensitivity analysis of the Pareto boundary. In this work, it is shown how going beyond sensitivity analysis can yield favorable process designs not seen by sensitivity analysis alone. This is achieved by taking uncertainties into account by worst and best case Pareto boundaries or by considering the robustness of the Pareto boundary with respect to uncertain model parameters as additional objectives. In order to increase computational efficiency, for the first time, an adaptive scalarization approach is used to deal with uncertainties in MCO. The methods are illustrated by the calculation of a NQ curve of a distillation column.