April 10-14, 2016
A high-fidelity model of a mesoporous silica supported, polyethylenimine (PEI)-impregnated solid sorbent for CO2 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 based on quantum chemistry calculations. Using a Bayesian approach, we calibrate the sorbent model to Thermogravimetric (TGA) data. Discrepancy functions are included within the diffusion coefficients for diffusive species within the PEI bulk, enabling a 20-fold reduction in model order. Additional discrepancy functions account for non-ideal behavior in the adsorption of CO2 and H2O. 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 model of a bubbling fluidized bed adsorber.
Acknowledgements: This work is supported by the Carbon Capture Simulation Initiative, funded through the Office of Fossil Energy, US Department of Energy.
1. “Bayesian calibration of thermodynamic models for the uptake for CO2 in support amine sorbents using ab initio priors,” D. S. Mebane, K. S. Bhat, J. D. Kress, D. J. Fauth, M. L. Gray, A. Lee and D. C. Miller, Physical Chemistry Chemical Physics 15, 4355-4366 (2013). 2. “Transport, zwitterions, and the role of water for CO2 adsorption in mesoporous silica-supported amine sorbents,” D. S. Mebane, J. D. Kress, C. B. Storlie, D. J. Fauth, and M. L. Gray, J. Physical Chemistry C 117, 26617-27 (2013). 3. "Upscaling Uncertainty with Dynamic Discrepancy for a Multi-scale Carbon Capture System." K.S. Bhat, D.S. Mebane, P. Mahapatra, C.B. Storlie. Journal of the American Statistical Association, accepted for publication.