Hierarchical Bayesian estimation for adsorption isotherm parameter determination. (16th March 2020) - Shih, Chunkai; Park, Jongwoo; Sholl, David S.; Realff, Matthew J.; Yajima, Tomoyuki; Kawajiri, Yoshiaki
Graphical abstract: Highlights: Hierarchical Bayesian inference was applied to multiple adsorption data sets. A multiplicative factor was employed to quantify differences between data sets. Outliers were identified which are not in agreement with other data sets. Isotherm parameters were estimated simultaneously while quantifying discrepancies. Abstract: Estimation of isotherm model parameters from experimental data is necessary in adsorption process modeling. To facilitate isotherm modeling, databases that store experimental data exist. Nevertheless, data inconsistency among different researchers must be resolved to find a single set of isotherm parameters from multiple data sets. Herein, we propose a hierarchical Bayesian estimation method to quantify the discrepancy by a multiplicative factor while simultaneously obtaining the probability distributions of a single set of isotherm parameters. This approach also allows us to identify outliers, which are data sets that are not in agreement with other sets. The proposed approach is demonstrated for the case of CO2 adsorption in the metal-organic framework UiO-66.