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Title: A framework for optimization and quantification of uncertainty and sensitivity for developing carbon capture systems

Under the auspices of the U.S. Department of Energy’s Carbon Capture Simulation Initiative (CCSI), a Framework for Optimization and Quantification of Uncertainty and Sensitivity (FOQUS) has been developed. This tool enables carbon capture systems to be rapidly synthesized and rigorously optimized, in an environment that accounts for and propagates uncertainties in parameters and models. FOQUS currently enables (1) the development of surrogate algebraic models utilizing the ALAMO algorithm, which can be used for superstructure optimization to identify optimal process configurations, (2) simulation-based optimization utilizing derivative free optimization (DFO) algorithms with detailed black-box process models, and (3) rigorous uncertainty quantification through PSUADE. FOQUS utilizes another CCSI technology, the Turbine Science Gateway, to manage the thousands of simulated runs necessary for optimization and UQ. Thus, this computational framework has been demonstrated for the design and analysis of a solid sorbent based carbon capture system.
 [1] ;  [2] ;  [3] ;  [2] ;  [4] ;  [3]
  1. National Energy Technology Lab. (NETL), Pittsburgh, PA, (United States); Carnegie Mellon Univ., Pittsburgh, PA (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  3. National Energy Technology Lab. (NETL), Pittsburgh, PA, (United States)
  4. Carnegie Mellon Univ., Pittsburgh, PA (United States)
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 1876-6102
Accepted Manuscript
Journal Name:
Energy Procedia
Additional Journal Information:
Journal Volume: 63; Journal Issue: C; Journal ID: ISSN 1876-6102
Research Org:
National Energy Technology Laboratory (NETL), Pittsburgh, PA, and Morgantown, WV (United States)
Sponsoring Org:
USDOE Office of Fossil Energy (FE)
Country of Publication:
United States
54 ENVIRONMENTAL SCIENCES; 97 MATHEMATICS AND COMPUTING optimization; uncertainty quantification; simulation; surrogate model; carbon capture