Upscaling Uncertainty with Dynamic Discrepancy for a Multi-scale Carbon Capture System

Uncertainties from model parameters and model discrepancy from small-scale models impact he accuracy and reliability of predictions of large-scale systems. Inadequate representation of these uncertainties may result in inaccurate and over-confident predictions during scale-up to larger systems. Hence multiscale modeling efforts must accurately quantify the effect of the propagation of uncertainties during upscaling. Using a Bayesian approach, we calibrate a small-scale solid sorbent model to Thermogravimetric (TGA) data on a functional profile using chemistry-based priors. Crucial to this effort is the representation of model discrepancy, which uses a Bayesian Smoothing Splines (BSS-ANOVA) framework. Our uncertainty quantification (UQ) approach could be considered intrusive as it includes the discrepancy function within the chemical rate expressions; resulting in a set of stochastic differential equations. Such an approach allows for easily propagating uncertainty by propagating the joint model parameter and discrepancy posterior into the larger-scale system of rate expressions. The broad UQ framework presented here could be applicable to virtually all areas of science where multiscale modeling is used.