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

The development and widespread commercial deployment of carbon capture and storage technologies will be instrumental in expanding affordable energy production and increasing the availability of CO₂ as a feedstock for several industrial applications. This development can be accelerated by applying computational model-based process optimization methodologies that explicitly account for the impact of parametric uncertainties to obtain solutions that exhibit minimal technical risk. Robust optimization (RO) is one such prominent methodology. In this work, we present a successful application of the nonlinear two-stage RO solver PyROS to a detailed rate-based, equation-oriented model for the economical design and operation of a monoethanolamine scrubbing process for postcombustion carbon capture under uncertainty in the thermodynamic property submodel parameters. Our application enables us to successfully obtain risk-averse model solutions for CO₂ capture targets ranging from 90% to over 99%, with solutions for capture targets of up to 98% only marginally more expensive than their nominally optimal counterparts. Thus, our results demonstrate that employing RO and the PyROS solver can help us obtain risk-averse carbon capture process designs without inherently unnecessary cost burdens that are often associated with ad hoc overdesign approaches.