(419f) Robust Process Intensification and Optimization: Application to Carbon Capture Systems
Even with rigorous deterministic optimization, uncertainty in system performance and cost remains a barrier to timely industrial adoption of otherwise high performance designs. Furthermore, in novel systems full probabilistic characterization of uncertainty is at best case limited, reducing the justification to employ stochastic optimization approaches in these cases. In this work we apply a recently proposed optimal control formulation for intensification of a pre-combustion CO2 capture system with the aim of consolidating thermodynamic operations to reduce overall costs. Assuming novel system design efforts for which no useful uncertainty characterization is available, we extend recently developed nonlinear robust optimization techniques to develop steady state intensified designs that operate feasibly, safely and satisfy all performance criteria under parametric uncertainty. A central objective of robust optimization is to generate designs conservative enough to ensure feasible operation amidst all uncertainty realizations. However, solutions to current nonlinear robust formulations often rely on approximations that result in a degree of over-conservatism that may offset any cost reduction provided by process intensification efforts. We propose flexible statistically-derived âback-offâ constraints that ensure feasibility yet eliminate the over-conservatism of most current approaches. Moreover, we propose to leverage the aforementioned method to directly and simultaneously mitigate the worst possible impact of user-specified uncertainty on the objective function.