Novel Optimization Models and Methods for Process Systems Engineering Under Uncertainty

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In today's highly competitive world, companies must achieve efficient and effective design and operation of their systems while satisfying stringent constraints incurred by the concerns about sustainability of business and the natural environment. These challenges require novel computational methods to handle complex large-scale systems that are subject to uncertainties and nonlinear models. Optimization models and algorithms play a central role in the development of these methods. The proposed research program targets the development of systematic and efficient approaches to optimization techniques to solve complex process systems engineering problems subject to uncertainty, which cannot be satisfactorily solved with the current technologies. The major goals are to develop tractable problem formulations to address a huge (sometimes infinite) number of possible uncertainty realizations and to develop optimization methods that can guarantee global optimality for large-scale complex problems.