The prevalence of multi-billion-dollar mega-projects have substantially exacerbated the potential risks associated with chemical process safety and environmental failures. The use of model-based approaches to ensure rigorous satisfaction of safety and environmental constraints remains an open area of research within the process systems community . For the general nonlinear case, these problems require the solution of a series of global optimization problems [2,3]. While global optimization techniques exist to address a wide-variety of model-based problems, large-dimensionality systems remain an open-challenge . One approach to mitigate this “curse of dimensionality” is via the reformulation of the problem into a reduced-dimension space via a simulation-based implicit approach. We’re currently investigating the creation of novel fixed-point iteration schemes that will further reduce the computational cost of the methods presented in [1,3] specifically for large-scale physics-based models.
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