(707b) Minimizing the Economic Impact of Amine Scrubbing Using High Fidelity Modeling and Optimization | AIChE

(707b) Minimizing the Economic Impact of Amine Scrubbing Using High Fidelity Modeling and Optimization


Baldea, M. - Presenter, The University of Texas at Austin
Pattison, R., The University of Texas at Austin
Tsay, C., Imperial College London
Rochelle, G., The University of Texas at Austin
Walters, M. S., The University of Texas at Austin
Frailie, P. T. II, The University of Texas at Austin
Minimizing the economic impact of amine scrubbing using high fidelity modeling and optimization

R.C. Pattison, C. Tsay, Y. Zhang, P.T. Frailie, G.T. Rochelle, and M. Baldea

McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712

In an effort to minimize carbon emissions from existing power generation units, amine scrubbing technologies have garnered significant attention owing to the energetically favorable set of reactions between carbon dioxide and amine solvents. A large body of research has been dedicated to this field, with a primary focus on characterizing the reaction kinetics, mass transfer properties, and thermodynamic properties for various solvents [1-2]. Process modeling and simulation studies have also been carried out, with a focus on predicting the performance and cost of the absorption and stripping units [3]. However, relatively little research has been focused on the holistic economic analysis and optimization of the entire process using detailed unit models.

Owing to the complexity of these systems and the numerous design decisions that impact process performance, identifying the most economically compelling designs is extremely challenging, and calls for the use of modern optimization tools. These must be used to consider all the design decisions and constraints simultaneously, such that capital cost and energy use are minimized while simultaneously guaranteeing that the process meets required removal specifications and operational constraints.. While detailed rate-based mass transfer separation models are available in various software programs, the corresponding equations are typically highly nonlinear, coupled and stiff; they are difficult to solve (particularly when embedded within process flowsheets), and thus, not suitable for process-wide optimization.

In this presentation, we introduce a novel framework for plant-wide design optimization of amine-based carbon capture processes. We describe a set of experimentally-validated first-principles and empirical rate-based models of the absorption and stripping columns, along with empirical but detailed capital cost estimates for the process units. The numerical underpinnings of our approach are derived from the pseudo-transient continuation methods and the relaxation-based flowsheet optimization algorithms introduced in our previous work [4-5].

We demonstrate our framework with two extensive case studies, considering amine-based carbon capture plants having different carbon dioxide removal requirements (90% and 99%) from flue gas. The design optimization objective function is designed to minimize the present value (based on both capital and operating expenses) of the plant in the two cases while ensuring that the removal requirements are met and operational constraints are satisfied. The results show significant cost reduction when compared to a heuristics-guided design.


[1] Frailie, P.T.; Plaza, J.; Van Wagener, D.; Rochelle, G.T. Modeling piperazine thermodynamics. Energy Procedia, 2011, 4, 35-42.

[2] Sherman, B.; Frailie, P.T.; Li, L.; Salta, N.; Rochelle, G.T. Thermodynamic and Kinetic Modeling of Piperazine/ 2-Methylpiperazine. Energy Procedia, 2014, 63, 1243-1255.

[3] Frailie, P.T. Modeling of carbon dioxide absorption/stripping by aqueous methyldiethanolamine/ piperazine. Doctoral Dissertation, 2014.

[4] Pattison, R.C.; Baldea, M. Equation-oriented flowsheet simulation and optimization using pseudo-transient models. AIChE J., 2014, 60, 4104-4123.

[5] Pattison, R.C.; Tsay, C.; Baldea, M. Pseudo-transient models for multiscale, multiresolution simulation and optimization of intensified reaction/separation/recycle processes: Framework and a dimethyl ether production case study. Comp. & Chem. Eng., 2017.