(400d) Nonconvex Two-Stage Robust Optimization of an Amine-Based CO2 Capture System

Due to economic, environmental, and sociopolitical factors, process and energy systems with reduced carbon footprints are becoming increasingly prominent worldwide [1]. Carbon capture and storage technologies are considered key to minimal cost pathways for reducing net CO₂ emissions and thereby limiting anthropogenic climate change [1, 2]. The United States Department of Energy has, to this end, spearheaded initiatives for developing computational tools to accelerate the widespread industrial deployment of carbon capture and storage technologies [3–6]. Given its relative maturity and widespread commercial use as an acid-gas removal technology [2], amine-based CO₂ absorption has been identified as a benchmark for further development of these computational tools [3–6].

Mathematical models for amine-based CO₂ capture systems are often highly subject to uncertainty in their physicochemical property model parameters. Recent uncertainty quantification studies have found that uncertainty in the transport and thermodynamic property submodel parameters, in particular, may significantly impact the process model outputs [3-7]. Consequently, system designs prescribed by deterministic optimization under a nominal uncertain parameter realization may be suboptimal or infeasible under off-nominal realizations [3, 7]. It follows that an optimization under uncertainty framework is required to obtain minimal cost designs which are feasible in light of the parametric uncertainty.

Two-stage robust optimization (RO) is particularly useful for addressing the various uncertainties present in the mathematical modeling of process and energy systems. Often, these mathematical models feature nonconvexities, equality constraints which cannot be reformulated out of the model, and both design and recourse variables [7]. Methods for obtaining robust solutions to such models have been recently developed. In [7], the utility of a generalized cutting set algorithm for nonconvex two-stage RO problems with uncertain equality constraints was demonstrated for a handful of chemical process models. The algorithm has been implemented as PyROS, an open-source meta-solver for models written in the Pyomo algebraic modeling language [8, 9].

In this work, we apply PyROS to a high-fidelity model of an MEA-based CO₂ absorption system under uncertainty in the physicochemical property models. The uncertainty sets are chosen on the basis of a rigorous parameter estimation and uncertainty quantification study. Robust system designs are obtained for a variety of CO₂ capture rate threshold requirements and levels of uncertainty. Finally, a price-of-robustness study is performed to assess the response of the quality of the robust designs to the level of uncertainty adapted. Our application demonstrates the utility of PyROS for obtaining robust designs of an eminent process system with a high-fidelity optimization model subject to uncertainty.

Disclaimer

This project was funded by the Department of Energy, National Energy Technology Laboratory an agency of the United States Government, through a support contract. Neither the United States Government nor any agency thereof, nor any of its employees, nor the support contractor, nor any of their employees, makes any warranty, expressor implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

Acknowledgments

The authors graciously acknowledge funding from the U.S. Department of Energy, Office of Fossil Energy and Carbon Management, through the Carbon Capture Program and Simulation-based Engineering/Crosscutting Research Program. CEG and JAFS also gratefully acknowledge support from the Department of Energy’s Carbon Capture Simulation Impact for Industry (CCSI2) program.

References

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[6] Akula P, Lee A, Eslick J, Bhattacharyya D, Miller DC. A modified electrolyte non‐random two‐liquid model with analytical expression for excess enthalpy: Application to the MEA‐H2O‐CO2 system. AIChE Journal. 2023 Jan 1:e17935.

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[8] Isenberg, NM, Sherman, JA, Siirola, JD, & Gounaris, CE. PyROS Solver. Pyomo Documentation. 2023. https://pyomo.readthedocs.io/en/stable/contributed_packages/pyros.html

[9] Isenberg, NM, Sherman, JA, Siirola, JD, & Gounaris, CE. PyROS: The Pyomo Robust Optimization Solver. Forthcoming. 2023.