A generalized cutting-set approach for nonlinear robust optimization in process systems engineering

We propose a novel computational framework for the robust optimization of highly nonlinear, non-convex models that possess uncertainty in their parameter data. The proposed method is a generalization of the robust cutting-set algorithm that can handle models containing irremovable equality constraints, as is often the case with models in the process systems engineering domain. Additionally, we accommodate general forms of decision rules to facilitate recourse in second-stage (control) variables. In particular, we compare and contrast the use of various types of decision rules, including quadratic ones, which we show in certain examples to be able to decrease the overall price of robustness. Our proposed approach is demonstrated on three process flow sheet models, including a relatively complex model for amine-based CO2 capture. We thus verify that the generalization of the robust cutting-set algorithm allows for the facile identification of robust feasible designs for process systems of practical relevance.