Applying Sampling-Based Convex Relaxations to Dynamic Process Models ⁎ ⁎ This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) under Grant RGPIN-2017-05944.

Convex relaxations are a crucial tool in methods for global optimization, but are challenging to construct for dynamic processes. In this article, we investigate combining two recent approaches for convex relaxation generation in new nontrivial ways, to aid global optimization of dynamic chemical process models. Specifically, we combine recent approaches for automatically generating convex relaxations for solutions of parametric ordinary differential equations with a recent sampling-based approach for tractably generating lower bounds of a convex relaxation.