What question did this study set out to answer?

This work aims to optimize monoethanolamine scrubbing processes for post-combustion carbon capture under uncertainty.

June 18, 2026Open Access

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

Key Points

This work aims to optimize monoethanolamine scrubbing processes for post-combustion carbon capture under uncertainty.
Utilized a nonlinear two-stage robust optimization solver, PyROS.
Applied a detailed rate-based model accounting for thermodynamic property uncertainties.
Targeted CO2 capture rates of 90% to over 99% for economic viability.
Achieved risk-averse solutions with capture targets of over 98% that are marginally costlier than optimal designs.
Demonstrated that robust optimization reduces technical risks without excessive cost burdens.
Validated the applicability of the PyROS solver in commercial carbon capture processes.

Abstract

High Resolution Image Download MS PowerPoint Slide 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 CO 2 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 CO 2 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.

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