(419f) Robust Process Intensification and Optimization: Application to Carbon Capture Systems

The Intergovernmental Panel on Climate Change (IPCC) estimates that the world must limit global temperature increases to less than 2°C above preindustrial levels to avoid irreversible damage to the ecosystem. At current CO₂ emission rates, the IPCC also estimates global temperatures would reach this limit in just under 20 years. The largest point sources of concentrated CO₂ exist in the fossil power industry, motivating a rapid switch to zero carbon energy via CO₂ capture on fossil based power, nuclear and low carbon renewable deployments. While nuclear and renewable power generation is increasing, fossil based power generation will continue to play a significant role especially in developing countries and so CO₂ capture solutions must be provided. However, the cost and performance of state of the art CO₂ capture technology are insufficient to support broad scale deployment. Development of CO₂ capture technology for fossil power plant emissions must be extremely aggressive to be cost effectively deployed, especially at the rates required to limit global warming to less than 2°C. Developers must entertain novel chemistries and systems to significantly improve performance, however doing so often results in complex systems with novel components that tend to increase cost. Moreover, novel technology and complex systems often exhibit great uncertainty due to low maturity levels.

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 “back-off” 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.