Bayesian Estimation, Uncertainty Propagation and Design of Experiments for CO2 Adsorption on Amine Sorbents

Abstract Uncertainty quantification for CO 2 adsorption on silica supported amine sorbents in a packed bed adsorber is performed using Bayesian inference. Markov Chain Monte Carlo sampling is used to obtain the probability distributions of adsorption model parameters. The effect of parametric uncertainties on adsorbate breakthrough is quantified providing a measure of model reliability. Further, the value of an additional experimental data point in reducing the parametric uncertainties and thereby increasing the process throughput is also determined.