422300 Integration of High-Fidelity CO2 Sorbent Models at the Process Scale Using Dynamic Discrepancy

Monday, November 9, 2015: 2:10 PM
Salon G (Salt Lake Marriott Downtown at City Creek)
Kuijun Li1,2, Priyadarshi Mahapatra3 and David Mebane1,2, (1)National Energy Technology Laboratory, Morgantown, WV, (2)Mechanical and Aerospace Department, West Virginia University, Morgantown, WV, (3)AVESTAR Center, National Energy Technology Laboratory, Morgantown, WV

A high-fidelity model of a mesoporous silica supported, polyethylenimine (PEI)-impregnated solid sorbent for carbon capture has been incorporated into a model of a bubbling fluidized bed adsorber using Dynamic Discrepancy Reduced Modeling (DDRM). The sorbent model includes a detailed treatment of transport and amine-CO2-H2O interactions. Using a Bayesian approach, we calibrate the sorbent model to the Thermogravimetric (TGA) data. Discrepancy functions are included within the diffusion coefficients for diffusive species within the PEI bulk, enabling a 100-fold reduction in model order. The discrepancy functions are based on a Gaussian process in the Bayesian Smoothing Splines ANOVA framework, which provides a convenient parametric form for calibration and upscaling. The dynamic discrepancy method for scale-bridging produces probabilistic predictions at larger scales, quantifying uncertainty due to model reduction and the extrapolation inherent in model upscaling.  The dynamic discrepancy method is demonstrated using TGA data for a PEI-based sorbent and an Aspen Custom Modeler-based model of a bubbling fluidized bed adsorber.

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See more of this Session: Modeling and Computation in Energy and Environment
See more of this Group/Topical: Computing and Systems Technology Division