Bayesian Estimation, Uncertainty Propagation and Design of Experiments for CO2 Adsorption on Amine Sorbents
作者： Jayashree Kalyanaraman , Yoshiaki Kawajiri , Matthew J. Realff
DOI: 10.1016/B978-0-444-63433-7.50042-0
关键词:
摘要: Uncertainty quantification for CO2 adsorption on silica supported amine sorbents in a packed bed adsorber is performed using Bayesian inference. Markov Chain Monte Carlo sampling used to obtain the probability distributions of model parameters. The effect parametric uncertainties adsorbate breakthrough quantified providing measure reliability. Further, value an additional experimental data point reducing and thereby increasing process throughput also determined.
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