Robust Optimization of Nonlinear Processes under Uncertainty

Many important questions and challenges in process systems design, operation and control can be typically posed as nonlinear optimization problems. To date, most optimal decision making tools for such problems are mainly based on deterministic mathematical models, where all parameter values in the model are assumed to be known precisely. In practice, however, mathematical models are merely approximate descriptions of the real system, and parameters such as future demands, prices, equipment wearout and process conditions are subject to significant uncertainty. It has been frequently shown that disregarding such uncertainty can lead to severe performance losses, increased costs, and energy/environmental penalties. We propose to develop robust, local and global optimization methods for the efficient solution of such nonlinear optimization problems in the presence of uncertainties. Depending on their nature, uncertainties can be accounted for in a static/proactive or reactive way. Two important industrial applications will be investigated and the developed methods will be applied for the integrated design, optimization and control of process systems under uncertainty.