Bayesian estimation of parametric uncertainties, quantification and reduction using optimal design of experiments for CO2 adsorption on amine sorbents

摘要: Abstract Uncertainty quantification plays a significant role in establishing reliability of mathematical models, while applying to process optimization or technology feasibility studies. Uncertainties, general, could occur either model parameters. In this work, CO2 adsorption on amine sorbents, which are loaded hollow fibers is studied quantify the impact uncertainties isotherm parameters prediction. The design variable that most closely related economics sorption capacity, whose uncertainty investigated. We apply Bayesian analysis and determine utility function surface corresponding value information gained by respective experimental point. It demonstrated performing an experiment at condition with higher has reduction prediction compared choosing point lower utility.