Modeling, parameter estimation, and uncertainty quantification for CO2 adsorption process using flexible metal–organic frameworks by Bayesian Monte Carlo methods

Abstract

AbstractFlexible metal–organic frameworks (flexible MOFs) are considered promising adsorbents for CO2 capture, some of which have sigmoidal isotherm shapes that allow adsorption and desorption operations within a narrow partial pressure range. Nevertheless, modeling of adsorption processes employing flexible MOFs remains a challenge due to the unique isotherm shapes and kinetics. In this work, a Bayesian estimation framework is applied sequentially to handle two experimental data sets: isotherm and breakthrough measurements. The computational challenge for estimating the isotherm and kinetic parameters from the isotherm measurements and breakthrough experiments is resolved by Markov chain and sequential Monte Carlo methods. The uncertainties of the model parameters are obtained as probability distributions.