Designing a Robust MEA-Based Post-Combustion Carbon Capture Process with Capture Rate Guarantees

Abstract

This work presents an application of the nonlinear two-stage robust optimization solver PyROS to the model-based design and operation of a monoethanolamine scrubbing process for CO₂ capture under epistemic uncertainty. Through this application, risk-averse process designs are successfully obtained for CO₂ capture targets ranging from 90% to over 99%. In particular, the risk-averse solutions for CO₂ capture targets of up to 98% are shown to be only marginally more expensive than their nominally optimal counterparts. Thus, the results demonstrate the utility of recently developed nonlinear robust optimization approaches for the solution of large-scale chemical process models under uncertainty.