  Uncertainties from model parameters and model discrepancy from small-scale
models impact the accuracy and reliability of predictions of large-scale
systems. Inadequate representation of these uncertainties may result in
inaccurate and overconfident predictions during scale-up to larger models.
Hence multiscale modeling efforts must 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. We use an intrusive uncertainty quantification (UQ)
approach by including 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 may have far-reaching impact
into virtually all areas of science where multiscale modeling is used.
