Nonlinear Robust Optimization with Uncertain Equality Constraints

The problem of nonlinear optimization for process design under uncertainty is addressed in this paper. A novel robust optimization framework is proposed to address general nonlinear problems under uncertainty. To address the limitation of single point linearization with respect to uncertain parameters for a large uncertainty region, an iterative algorithm is developed. The new method applies local linearization around multiple realizations of the uncertainty, and iteratively solves a robust optimization problem. The proposed method can handle uncertainty in both inequality constraints and equality constraints.