Carbon Capture Simulation Initiative: A Case Study in Multiscale Modeling and New Challenges

Author:

Miller David C.1,Syamlal Madhava2,Mebane David S.3,Storlie Curt4,Bhattacharyya Debangsu5,Sahinidis Nikolaos V.6,Agarwal Deb7,Tong Charles8,Zitney Stephen E.2,Sarkar Avik9,Sun Xin9,Sundaresan Sankaran10,Ryan Emily11,Engel Dave9,Dale Crystal4

Affiliation:

1. US Department of Energy, National Energy Technology Laboratory, Pittsburgh, Pennsylvania 15236;

2. US Department of Energy, National Energy Technology Laboratory, Morgantown, West Virginia 26507;,

3. Department of Mechanical and Aerospace Engineering and

4. Los Alamos National Laboratory, Los Alamos, New Mexico 87545;,

5. Department of Chemical Engineering, West Virginia University, Morgantown, West Virginia 26506;,

6. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213;

7. Lawrence Berkeley National Laboratory, Berkeley, California 94720;

8. Lawrence Livermore National Laboratory, Livermore, California 94550;

9. Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352;, ,

10. Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544;

11. Department of Mechanical Engineering, Boston University, Boston, Massachusetts 02215;

Abstract

Advanced multiscale modeling and simulation have the potential to dramatically reduce the time and cost to develop new carbon capture technologies. The Carbon Capture Simulation Initiative is a partnership among national laboratories, industry, and universities that is developing, demonstrating, and deploying a suite of such tools, including basic data submodels, steady-state and dynamic process models, process optimization and uncertainty quantification tools, an advanced dynamic process control framework, high-resolution filtered computational-fluid-dynamics (CFD) submodels, validated high-fidelity device-scale CFD models with quantified uncertainty, and a risk-analysis framework. These tools and models enable basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to synthesize and optimize a process. The resulting process informs the development of process control systems and more detailed simulations of potential equipment to improve the design and reduce scale-up risk. Quantification and propagation of uncertainty across scales is an essential part of these tools and models.

Publisher

Annual Reviews

Subject

Renewable Energy, Sustainability and the Environment,General Chemical Engineering,General Chemistry

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