Hierarchical Bayesian estimation for adsorption isotherm parameter determination

被引：19

作者：

Shih, Chunkai ^{[1
]}

Park, Jongwoo ^{[2
]}

Sholl, David S. ^{[2
]}

Realff, Matthew J. ^{[2
]}

Yajima, Tomoyuki ^{[1
]}

Kawajiri, Yoshiaki ^{[1
,2
]}

机构：

[1] Nagoya Univ, Dept Mat Proc Engn, Chikusa Ku, Furo Cho 1, Nagoya, Aichi 4648603, Japan

[2] Georgia Inst Technol, Sch Chem & Biomol Engn, 311 Ferst Dr, Atlanta, GA 30332 USA

来源：

CHEMICAL ENGINEERING SCIENCE | 2020年 / 214卷

关键词：

Adsorption isotherm; Isotherm model parameters; Hierarchical Bayesian estimation; Probability distributions; Statistical analysis; METAL-ORGANIC FRAMEWORKS; CO2; ADSORPTION; SWING ADSORPTION; UIO-66; SEPARATION; GAS; QUANTIFICATION; TECHNOLOGIES; PREDICTION; REDUCTION;

D O I：

10.1016/j.ces.2019.115435

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

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. (C) 2019 Elsevier Ltd. All rights reserved.

引用

页数：11

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