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

Area of Science:

• Chemical Engineering
• Process Systems Engineering
• Environmental Engineering

Background:

• Carbon capture and storage (CCS) technologies are vital for affordable energy and CO2 feedstock.
• Computational modeling and process optimization are key to accelerating CCS deployment.
• Parametric uncertainties in models can lead to technically risky solutions.

Purpose of the Study:

• To apply robust optimization (RO) to a monoethanolamine (MEA) scrubbing process for postcombustion carbon capture.
• To assess the impact of thermodynamic property submodel parameter uncertainties on process design and operation.
• To obtain risk-averse solutions for CO2 capture targets using the PyROS solver.

Main Methods:

• Utilized a nonlinear two-stage RO solver, PyROS.
• Employed a detailed rate-based, equation-oriented model for MEA scrubbing.
• Incorporated uncertainty in thermodynamic property submodel parameters.

Main Results:

• Successfully obtained risk-averse model solutions for CO2 capture targets from 90% to over 99%.
• Solutions for capture targets up to 98% were only marginally more expensive than nominal optima.
• Demonstrated that RO with PyROS avoids unnecessary costs associated with ad hoc overdesign.

Conclusions:

• RO and the PyROS solver effectively yield risk-averse carbon capture process designs.
• This methodology accelerates the commercial deployment of CCS technologies.
• Achieves high CO2 capture rates without significant cost premiums.